{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import random\n",
    "from nltk.classify.scikitlearn import SklearnClassifier\n",
    "import pickle\n",
    "from sklearn.naive_bayes import MultinomialNB, BernoulliNB\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.svm import SVC\n",
    "from nltk.classify import ClassifierI\n",
    "from statistics import mode\n",
    "from nltk.tokenize import word_tokenize\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_pos = os.listdir('train/pos')\n",
    "files_pos = [open('train/pos/'+f, 'r').read() for f in files_pos]\n",
    "files_neg = os.listdir('train/neg')\n",
    "files_neg = [open('train/neg/'+f, 'r').read() for f in files_neg]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12500"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(files_neg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_words = []\n",
    "documents = []\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "import re\n",
    "\n",
    "stop_words = list(set(stopwords.words('english')))\n",
    "\n",
    "#  j is adject, r is adverb, and v is verb\n",
    "#allowed_word_types = [\"J\",\"R\",\"V\"]\n",
    "allowed_word_types = [\"J\"]\n",
    "\n",
    "for p in  files_pos:\n",
    "    \n",
    "    # create a list of tuples where the first element of each tuple is a review\n",
    "    # the second element is the label\n",
    "    documents.append( (p, \"pos\") )\n",
    "    \n",
    "    # remove punctuations\n",
    "    cleaned = re.sub(r'[^(a-zA-Z)\\s]','', p)\n",
    "    \n",
    "    # tokenize \n",
    "    tokenized = word_tokenize(cleaned)\n",
    "    \n",
    "    # remove stopwords \n",
    "    stopped = [w for w in tokenized if not w in stop_words]\n",
    "    \n",
    "    # parts of speech tagging for each word \n",
    "    pos = nltk.pos_tag(stopped)\n",
    "    \n",
    "    # make a list of  all adjectives identified by the allowed word types list above\n",
    "    for w in pos:\n",
    "        if w[1][0] in allowed_word_types:\n",
    "            all_words.append(w[0].lower())\n",
    "\n",
    "    \n",
    "for p in files_neg:\n",
    "    # create a list of tuples where the first element of each tuple is a review\n",
    "    # the second element is the label\n",
    "    documents.append( (p, \"neg\") )\n",
    "    \n",
    "    # remove punctuations\n",
    "    cleaned = re.sub(r'[^(a-zA-Z)\\s]','', p)\n",
    "    \n",
    "    # tokenize \n",
    "    tokenized = word_tokenize(cleaned)\n",
    "    \n",
    "    # remove stopwords \n",
    "    stopped = [w for w in tokenized if not w in stop_words]\n",
    "    \n",
    "    # parts of speech tagging for each word \n",
    "    neg = nltk.pos_tag(stopped)\n",
    "    \n",
    "    # make a list of  all adjectives identified by the allowed word types list above\n",
    "    for w in neg:\n",
    "        if w[1][0] in allowed_word_types:\n",
    "            all_words.append(w[0].lower())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29739"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_A = []\n",
    "for w in pos:\n",
    "    if w[1][0] in allowed_word_types:\n",
    "        pos_A.append(w[0].lower())\n",
    "pos_N = []\n",
    "for w in neg:\n",
    "    if w[1][0] in allowed_word_types:\n",
    "        pos_N.append(w[0].lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pos_N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3EAAAHFCAYAAABcsHAGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsvVeTXEmapve4Hx0qI1ILyNKtpne5zRkjZ8eMZrwi7/h7yb2irc1y16a7p3VVVxU0UoeOONLdeeEnIjORSCBRQBWUPzAggUTGUSHOec/3fe8rjDE4HA6Hw+FwOBwOh+P9QL7tDXA4HA6Hw+FwOBwOx/VxIs7hcDgcDofD4XA43iOciHM4HA6Hw+FwOByO9wgn4hwOh8PhcDgcDofjPcKJOIfD4XA4HA6Hw+F4j3AizuFwOBwOh8PhcDjeI5yIczgcDofD4XA4HI73CCfiHA6Hw+FwOBwOh+M9wok4h8PhcDgcDofD4XiP8N/2BgAIIczb3gaHw+FwOBwOh8PheFsYY8R1f/adEHEOh8Ph+HGRnqC5GtLshUhf4Pln5wljwGiYDQomxxmqdPfVHA6Hw+F4l3EizuFwOD4Cglhy69dd7v7jGnHbJ2mfffyryqBLw3f/3yl/+S+HzAbFW9xSh8PhcDgcL8OJOIfD8d7gBQI/9DDGUOUarVzF6LoIKYg7ASvbMc1eSLMXECY+YcOrfwCm/Zy//9fjt7uhDofD4XA4XooTcQ6H471ASEg6ASvbCVWhGD7NyGfV296s94Yy1zz+44j5sCRu+cQtn83PWmx+2qK1FtHsBW97Ex0Oh8PhcFwTJ+IcDsc7j/QEXiBorUVsftYim5TMh6UTca+AKjTH3085fTgjiDyCWJJO1vBDifQESceJOIfD4XA43hdcxIDD4XjnCRKP1nrE+p0GN3+1wtZnbaKmuwf1qhht0JWhzBTZtKJIFWWuUJUBXGuqw+FwOBzvC+4qyOFwvPOEiUd7PWLtVpPtL9r4kXc2y+W4NsaAUcbOEhZQZoqq0BilnYZzOByOdwEpEVIiPB/h2RlwMJiqwpTl2946xzuEE3EOh+OdJ2r61pBjLcQPXQOBw+FwOD5ApMTvrOB3Vgg3twk3tzBFgS5yssePyB7dd0LOscSJOIfjTSNACOsGKKRAnIttFMJWQ6D+agxGg9bmUiVESJBSgDj/82f/J6RYrM5+29jlGG0u/OwLN7VejhACIZebv3yozQ97tWXa/Rfnln2278v9qLfX1Pu/XP6z+14/vtEN6O01aK9HePUMlx9JgviioDvb3svLvLDPi+0TZ8dX1H8snx+92D67vOuwfN7rr1yx78vtrL+eP67SO7dtgF5Uzl60Tq9+DoXdZq2uv82vy/nn+crj+YLn+vIC7TGQUpw9xthjJLzL7ymol68Nul6+w+FwvI8IKfFabaKtHZpf/ZzGFz9Hz2eo2RQM5PtPnIhzLHEizuF4g3iBIEx8mmshvb2E9kZE3PQJEs9enAuBqjSq0GTTimxaMXicMng8p8wUZXHW1tbdSdj6rEWQeKjKUMwq5qMSP5T09hJaaxFBbAVNlWvymWLwNGW4nzI7LV6Y9SV9gR9IWhsRq3sJ7Y2YZCUgrLdTa0OZKtJRuVxmOirJJi83EmmsBHS2YtrrEe31iLjjE8Qenm8Fl9GGqtAUc8XkOGN8nDM6yJgc58tlrN5ssH6nSbMXkqwErN5osHa7SWcjorkasvMl/Ob/usHkP+cX1l1k1rVyuG+P6fjo7P+FtOKgvRGzshXTXA1p9AKixMevj6MQAl1pykwzHxUMDzImRznT0/zl+y6gtRHR2Yhob9h9D5t236VcCDJNlWuyScXkJGd8lDE6yJgP7UnZjyRbn7VYv9MkTDz8yOPxn0Y8/uPwSlHW3ojY+KRlX2stn3RU8ugPQ/qP05c+V6+D9AR+LFnZjGlvxjR7AY1uSBBJgtizgk5ClWvKTDE5zhkd2ud5eppT5c/foaQdsPeLDr0bDWanOdNBQTqyJjZrt5qs3WwQNX3Cpm2nPQspzxk+Tek/nlPM1Y+67w6Hw+FwvG2ciHM43iBeIEk6Puu3m9z9TY/tLzt0tmwulx8IhCcoU0UxV4wOrdi49z/6ZJMSA3Y+qV5Wdyfh8/+8TqMXUqaKyUlO/9GcqOlz9zerbH3eJukE+KEkm5RMjnPu/3bAw98POdSTl4q4oOGxutfgk39cZeerDqs3GzR7IV4gUJVhPsjpP065/28DhLTi6zpCptEN2fqsxc5XHba/aNPbTUi6AUFsL7p1ZcinFdOTnP2vJzz92xitzCUR9/n/us763RZrNxskKwFR0wpMgNZayPYX7UuFwfmg4MHvhjz43YAq1xdEnPQE0hd0d2Ju/GKFjU+tWGqthTQ6AV5kxVaZKeajktOHMx79YciTP48pc3UtAdtej9j5WYedLzvsfNmmvRGRrAR4gRWwVa7JpxXD/ZT9v4158pcxZaqWIi6IJNtfdfjyXzZo9ULrGGng6V9GqCsqTK2NiDv/qcfuz1fobsecPpwzOc1/VBFnq38QNexrfffnHdbvNFm/3SRZCUhWAqS0r/dsUjIflez/bcyjP4zY/9uYbFJeKeLijs/df1zj7m9WOfpuyvH3UwZPrCC//R97fPa/rFuRvBHa6rMyHN+bsf+3Mfd/O2A2KJyIczgcDscHjxNxDscbQkhY2Y65+5tVNj9r01oLqXLN4TcTVKWXLXzyXEuYrVjYi3td6YuiRABCEDV8mt2QZi+k1Qspc82sX/DgdwP8QOJHkqjl44eS9Tv2IjqIPcrUOhCeFx9CgvQlG3ea3Phll41PmvT2GggpOP5+ykFlwBiEtO2KQgq2v2jTWg958NsBMCKbPL8it7ITs36rydYXbXa+atPohhgDp4/mqO/1ssVP1O1yqtRM+zlFWqHKixf0k+Ocp38ZMz7KOfxmQu9GwtqtBo2VkLDhMRsUHH8/ZTa42FZSzCtOH8w5eTBjPrwoYm0rH8Qtn+5eghdIJsc5k+McY4w93FIQJB5JOyBuB9z4ZZe45aOVrUwWqbosPgSs32qydrvBzlcddr5qE8QexVxx/P2MqtQYZVsIhQTPk2RTG5FQZgr1TKukqJ8n5MWWzCtfd7BspVz+ftmDXhMDy+ey0bMB4lob+o/nmIe2/XHxGo/bAUnHZ2UrJvxHjzCWFHPF4MmcIlXo6rI4FcJWtZu9AH2rQXsjoswUUdPn9OGMwdM5ni/wfIkXSKanOeOjnHxaPXd5DofD4XB8aDgR53C8AYSwM1wrWzGf//MGW5+1mA9LW3H5eszg8RxdX/QmnYBGN6C726CzGWGAsqht3p9z/Rk2PDobEdK3bZTD/Yynfx0zeJKiK00Qe8vQ5vW7TW79uktZXySP9jPyabWcQZKeWIq9n//vm/RuNBAChvsZh99OGTxJqQpFGHus37WVlZ0v24TNLkbBrF8weMJzRVx3J+HTf1pj75crbH3RpkyVzSV7MGPwNGM+KjDK4IWS9lpE0PCYnubkc4UqL+745CjjcWXwI4nnS3Z/1qHKNas3bRvpyf0Zf/kvRxzfm154nK4M+cy2qebTi9tojMEoiFo+vb3EVkMPMmb9nGm/QJUagaC7l7D3iw5rt5qs32qwshUxOsgYPk0x+nIboBCC9TsNvviXDba/7LD1eZvRQcrxd1P6j+cMnmYUswqtDVHDp7UeIoRtAbxKxLzzGEAbhLSV1852zOQo5+TpnFk/Z9Yv7Gwkgu0v29z45QqdrZidr9oIT3D6cE42KVGlvnL/pSetaI89woZPEEkOvp1y+O2EdFSSjiuipkfc9lGloZjb512pn2gY0OFwOByOt4gTcQ7HG0D6giDyiFo+SdtHeoLpac7xvWl9MZ9awwVh7fLDhs/pwzlJJ+Do2yllpq40r/B8SdjwSccl+19POPhmwvG9GZPjHK00ni+Z9QtGBxmf/tMayc/tBfPNX3fBDBkeZlAvu9EL6e0mbNxt0dmMqTLFwd/thfHx9zOmJzmqMniBYHJqZ7aEhK3P23R3Y279uoeuDIMnZ616QSwJEo/eXsLWFy3its/oIGXwKOXxn4acPpozH5YUc4XRBukLhq0UP5Tkc0U+q0jHz1TUUgX9HOlJhITOZkQ+qygze9G/mKcbPs0uPM4YO2+nSn2pumdq45eTBzO+/n+PqTJNOi7JpxXZrEIrW42bDQr7vXHFnf/UI2z6tNbsnFuRVqTjWhzWz2Xc8lm9Zatw0hOc3J9x8M2YJ38cMTq0825VoTDazrzFT3wQgmJu9zufv5+B5cZAPlPs/21MmSnScb0/s2oZwi6AMlek45Kb/9DlTrdH0vZpb0Y0D0LSSUmZPV90SU+QdAOM8uk/mtN/NOfkwZzThzOKuirqh3b+zmiDKjXz0dVtmg6Hw+FwfEg4EedwvAGkZy8mw8SamGhlGO6nHP59ytH3M1vF4cyxT0ixdOCrCkWZX53TtXBizKcVD3434MFvB0z7VmhQt+gd35vR+W5KsxeycdeaXNz+j10rwv5wtujWasjOVx027jZp9EKOv5/y3X875fGfhmezRMZWDI+/n9J/NKe9FtHdTehsxvj/k2TwdH5h+8I6iHv1RsLmZ7YC9/QvYx7++5D7v+0z2k8vuiWec3BcOD8+21JY5pqqNMumwHRcUqSqrtzo5dza9PSisQmcORleckCsv3f49yn9h+nSeXLpaFj//Ogwo/8kpZgr1u82bbTBakhnI6rn9uw6hbBVvfZmxNrNBluftzn6dsrTv4548LsB9/+tTzourThfbEtdsV1s58ucJ99lrIirePj7IU/+Mrb5c/qym+f0tOD43gwvlOz+okPY8GnXBjXD/avn9qQn7Ewghm//9ZQ//d8HTE8LZv3CrsectY8utud9Pp4Oh8PhcLwKTsQ5HG8ArWwFKJ9VzIcFYeKxsh2z+7MOQgrChrd0dywzRZle33hBa4OqDNmsYnJsZ3+KefVMC6JmHsil46EXSLo7CUk7uGDHHrcDejcaNLohqtDMBuXSMfBSa18O6ahkcmrb45JOQHc3IekE1uikto6PWjbDrbUWETU8ZqcFx/emnNybMj3JyWc/wGSiDqVebI0VgbUlP693wV7l+oXVGq0NqjRM+zllZrfdDyVBw8Pzzw6mkFZk9HYTmr2QIJak44KDbyacPrQzeVdVmT4UjK6rpi94PafjkqrQpMMSVWiEhDC2rp3Cu3p2zxhDVVon12k/Z/AkXVbgHA6Hw+H42HEizuF4A+hKk88M82HB+DCnvRGz9Vmb7q6NGTj8e8zx/Sn9h3Om/YKquP7FvVGGKlMUddthPrXzVc+ilGY+sHN4qzcatDYTwoZflyrsz0dNn+5OTNTyreAcFKSjgrxudby0zMpYIXeSk6wEVqi1/GUMgVEQtwK6OwmNXoj0BOmk5Pj+jP6TdCmC3ie0MhRpRTG3JiaLFlA/vCg6hBQ0Vmx+XdwJEEIw7Rcc/H3C6DCzM44OqlLXx1QtW1y90BqSyEuBb2cYDUU93zgfFMxHpcuAczgcDoejxok4xwUEAg8fTwR4eMj6t4eHEBKBRCy97xZ/E+cCog22fmKWv7TRGDQaha6/KhTaqOXfr5ci/e5i6srR5CTn0R+GlLmid6NB3PRp9EK2v7QOjxt3m0xOCqYntro17ecUc/VCS3Rd56qVua1KXFV9MhrKzDpSGgNRw8MPJVKCqp8gP5TELZ+45eMF1ijl039aY/1287nLjFo+O192bAZZ28YZ+IG9AKfUqNqoJG77hLGHkIIqV1YcjstLhiXvAnHbJ24HxC2fqGmz2LxAIn3b3roI2t6426S1Hi1D2+VzgtuDyBprBJGNTyhSxaxvXRLNT9nW9+OaUV6J9ARxxydpB0RNezyXAq2OGFiEgO/+3LZSqqpEytqB8wXbbbStxOXTs1lIx9vBngN8AhEREuMJH4msP/ftp3tlSioKCpNRUWLe8890h8PheNdxIs5xDoFAEoqEWDQIiQlFTEBEKCI84eMRIM/9EsI7Z2dulid0K9A0mgplKirsCb40BWV9oi/IKEyOJvtgTviTo5xv//WEkwcztj5vs36nSW8vYeNus26ttDNC46OMJ38a8eTPI4YHmW0Ru+IQmLpVUxX6ysBn+4MGVWmq3IpiL5J4gahDrK3QlN6ZAUvU9IlaK6zdatiQ8ecgPbG8OA8i70JVymhQpapn9jy8wK5HV6a24n9+de9t01oLWb/TYvVGQu9Gg2bXxgkEsRUgCzEXNe2sX5kqG1z9HKQvbJh33WapCk0xUzbv7yfb9fqWyguqWj8WfijpbifL13lvL7ECuW0jL7zwTMw1uwHNXmhnOa+xrcZQu06qSyY1jp8Wn4BIJLRFj45cIxYJvogQCLRRlORkZs7MjBnpE5SZAPqD+Vx3OByOdxEn4j5yJJKACF+EBIQEIiQSDWLRrP8dLb/vEeAJD4FX1+Pqypw4q8QZDMZotKgrb0ahhEJRokxJKRZiLqc0BZmZkZo5uZmTk2J4vy/WilRR5ooysxfy6bhkPizo7lpr/Ead4ba618AoQ9T0efzn0dUZZGCNQOrMuBdzVvUAYDFD9sx11NLQo9KoshaIV8yIKaCcK6bH9t9lrhgf5vV82rkFP7Mem1t2Vp996whodgMa3ZCdn3XY/VmHpBMQJrYKpyqDSStELpbH2uiAxkpYB7FdvejFbCCwzAJ8bT31CodNSJaVRCF/GiHnBZJmL6CzZec+Nz9rEyUeYWNRjbWvLbEQwAL8QNJcNa9UNbRzjy+5eeH40RD153wiWnTkGm3Roy17RCLGJwTACE1pCiLRwNchRtgnKzcpJZeNhxwOx4+M5yGjGC9O8JotZBwDYLRCTaeo2RRd5JiieMmCACERvocMQ2ScIMMIEQQI30cICVLU53+NqRRGVZg8R+c5usjQef4cl7HrIzwf4fvIJEHGMdIPEEEAUiKkrM+VBqMUplLoskDndr06z0Fdw/1ZCISU+L1Vgt6aXZaqUJMx1WgICEQQIKPYbkcY2u3y6nYSs1h/icrzev0ZJv/xP/+ciPuIEUh8Appihbbs0hBtEtHGFwEBIQKJFLIWbYs2yrP2ybN2ysXy7HeMfSQGDyPsFa7GYMSirdJg6kpdaqbMzYShPqavD6m4xofKO47RkE0rTh/OmRzn7P9tTNwOaK9HrGzFNs/tTpONT1rs/KyD9CXTk4LJSc6szC9dsAop8AJbVRPyBSsW9uI6iG1r38Jq/7wz5NKAZVpRporBk5Qnfx491+XxeajKcPD1hOpcW6dWhiq3pijGLKpTEj+Udm7uHWiDEwJ6ew32frHC3i9XuPHLFcZHGf2Hc8bHuc2rmymqOq/PKMPmZy1+/X/u0NmMrxRVqjI2qL0+Fl4gCBOPfCrRSv0k4sNWVyVBHc7+UxA2PDY/bbH3C3ss1243OX04o/9wzuS0YHpq8/SqQtVCDD7/53X+4f/YeWd0vePlLNoo27LLpneDhBaBCJF4COTyp4Tw8AnxZYAApPEY6iNK40Scw/FTI4OQoLdKuLlNcusO4camvaFb5KQP7pE9uk856FNdQ8QJ38OLE/xuj3Bzi6C3htfq4DUaVsx5HmiNUQqVztHpnKJ/Stk/peyfoItTMD9wNl5IZBThNZp23Ztb+K02fqtj1x0EVkBpbYVTOqccjZbrLvsn6GtE+AjPQwQBjbuf0frVf0AXBXo+Y/7935l9/VcA/M4Kwdo64eY2wUrP7n8YIoSw688y1GxK0T+hPD2hODm2x/dHbslxIu4jRCLxCAhFbO+wilXasleLuKadgxPeD17+tdq66h8RWlCZCo/ggiB831GlNQRJRzb/zPMFSdfa1Bdzhao0N/+hy+YnLQ7/PmVlO7azZMPiUgviol0xjL1ly5+uLlcnpCeIGj6NrjXZyCZlbcxx9jNlrpgPS6KmT9jwmA2sEUf/4dxW6F7yeWO0zVFTpV5uZ5Ur5nXemdGGIPZor0eMj3LUSY6uXt/cxBi7bow5q3a9wuOFFLQ3Y3Z/3mH9VoPmasjgyZyTBzNOH84ZHWZkU+scapRByDrW4QXOmsZAmdrnrKgNXKKmT2czpiw0ZaFfuZ3UYEWxKo2dTw0F3qLCJp4Jg6+rrn49lxc1/WVb549NEEl6NxrsfNmht9cgavmk44qDv08YH+aMDjPrwpqpZaTE5qct1xb5nuHhE4mERLRpiS6hiHj25h1QyznftldKhdaKmRg5we5w/IQIz0OEEUG3R7Rzg/jGTeKbtwlW11DTKeVoaEXXNYSF8H1EGOG3OwS9NcL1DcLNLfzeGn6rjUwSW42SEqiFVJai0hSvvYLfWcFLGsggpJpOULMpqGteC0hZV/4aBKurhL11gq0twg0r4rxmG+F7CM9n0QakiwKVpfijIX6ng9dsIqOIatBHzWboPLt6fUIgpI/fWyW5dRdd5Kj5HDWbkh/sI4OAcHPb/t7YqvctuSBidZGjZjO8dgev2UaGIbnv2arnbHa9/f4BOBH3kWGrbyGJaNGVm3TkKoloEtHAFz6yPhH/VFSU5GZGSf7et1K+CK0M2aRElZpirhgepCSdgN2fdYiaNo5g1i+e24IoPVvZilo+SccacuTT6qLDpbBCsdELWdlJwMDkpKhDl8+Wl08rhvspcduKPekL8lnF5DR/oWnKecpcW3dMc7bM0YENtdbKkHQC1u82mY/KZcXvdTHazvtpDcKzc36v8jIVAhrdgLXbTbzQRjEcfjvlwe+HTI4yyuysaunHkqRpRZEXiCvbKY02pGPrBppNSowxNHshm5+3qErrFPrKAlbbCmqZKpBWFPqxh+fLZYVwgZQC6UuipkdzNSLpBvxUbyEvlLTXIzrbMVobhk9TDr4e8+C3A4pMUab2NaKVqY10AsKG98rPm+PtEoiIhmgTEiN5iRMN4OERiyalKJbtlg6H46dBRjHB2gbRzh7J3U+JdnbxkgY6y8iePCJ7eI/8YJ/i+Aj9kiqcTBKC1XXinRvEt24TrK4jkwYyiqx4kgLq9kk8DyGlFW1RjNdoEG5sEa5tUG7tkD68R3r/e3SWXk9Aej5+d5VwY5Pk5h2ivZt4SYKMEysuPR+jbeukkBLheXa7ggAvTmwVcn2TcnObfP8x6b3vKU4L0Nc7QQo/QCa2+hht7eCvrJDc+RR/pWvbSWshbKqq3l7PblsQIRsNgtU1gm6XYHXNVj5n96613h+CE3EfFQKfoJ5vWKUnN+jINXwCfBG8lS2qTEnKjNK83+Ym0hPWuTGy7YxC2otxXdkLWXNuFKjMla2S1SJsEfx9pVufsEYbcdtn7XaDWR1jkI6tcJBCENS5dN2dmNZqyPBpyuBJynxYXvjMTMclJw9mNHshqzcbRA2f5mpIoxuSjmygtjkXpCw9KxSkZ/u+tbYthOefqnxWMT7MmJ7kzEclfijZ+KRZu25WSF8sBZLdH/C8RXWptpKfqxfGEajSUMwUqtDIWty01iNa6yGqqPPiFqHP6ixE+7wo9QJJ2PDq5WmKVNnIhvmi+gZB6NHo2siE3m5CEHt1BfDyNhltSCdWxE1PC7JxRdz22fmqQ1VosklFOiqpzlUthRD17Jpdhq6sccfitWCMoZhVzAYFq2WDIPForYWs3mrYit/MOo8KKYgaHnHHRhw0VgKCyHtpdIUQQO2yKaQV/vLccyGkqN0lxdk8pTaLsYMlsq5U2uNjW0rzmarDza2j6qLNs70R0d1JaK9HSCncfNt7xKISF4hw6Uz8IoSQBISEIsZzlxcOx0/CogLn91aJdveIb9wm2t7Fb6+g5lPK/inZ4wekD+5RjUa2KnYVUloR1ekS794gvnmH+OZtvGZzOWdmygm6KDBlgakUIvCRvo8IQkQ9Oxe2V5BxhN9ZQVcl1WhINRqi0vnVYkoIhOfjNZuEG1skt+y6o+1ddGHXrbIUUxTossAUhd33et0yDJFRbCtljSZes4XwfarpFJ1ny8de53hKEeF3V4lv3MRrtPA7Kwjpoeaz5foxBuEHiDA4m0FMmnitDjIM8ZIGaj6nODm2x+o6M4iviPuU/Wiwc2qxaLAqt+jKTRqijU9Y32F9O1QUZGZK8Z5X4sKGR2stYmU7pnejgecLpqc56aikzJRtj5NW6NmfSVi9kWAMZOOS4dPUirLntN8ZbVClptENufs/r9LshRx8M2G4n6FKjedLejesQ+DWZ23CxGNykvPw9wOG++mFZU5PC/b/OqbRDdn6rEVrLeTzf96gt9fg5P6MyXFOmdo5Ji+UhLFHshLU1vCaKlMcfT/l+Puz9oB8rtBHOf3HKSf3Z6xs2Yy8qGFbNhfZePmsgtohM2rVYc/CCqrje7at8SrKTDE5zcmmJdITdDYjbv6qix/aecIirZCeWLY45vOKbFKdRTcYKNOK2WlOczWi2Qvp7VrX0LjlU6bKRiW0fHp7CZuftdn8tEXc8mtX0Oc8L/VzZ7Sh/2jO8f0ZQSy5+asVwtgjaviMDjI7H1a7VfqhtNW1SCIEZJOK43tTxod2dkgrw6xf0H88Z+PTFl4g2fykxVf/2wb9h3P6j1LAIH1Jdydm/U6LzU9bRC3fCtar7nLW5jieX7tv+qIWtTYeQfrWoCiIJEknIJtU6MpWP1V1zlykXrxWhnxakU1KGt2A9kbE6o2EjU9a1qQnU3WEg8/GJy22Pm+z+UkT4QlU9eptpo63w6L1/lXOEQvDq8WvRdSMw+F4XZ7/PpJJg3Btg2j3BsmdTwg3t5BxgppNbSXo8X3yg33KQR9TvqQCF0Z4zRbR9i7J3c8IN7frZc3ID/cpjw8pBwPUbGKNTIyy8VO+j9dq47c7RHs3ifduIv0Q0e1ZEZam5E8fke8/vbK1UXieFXBrGyS37pDc/RSv2cIoRXF8RH7wlGrYpxwMrIGKqqy5ihBWtLXbhJvbxHs37b9bbSsGpxMA8v0nVEX/5YdZ1ufD7irSD1CzCfnhPtVwQNk/Qc3nGGVdwIX0kHFM0FuzVdDdPcK1DbxmC6Qk7O8Qjwb1nN7pG5+RcyLuI8HDW7bGdOQaK3KtzoO7/ktgeSI250/Lz74gz0xPlv8SF//HLsLaT1uHypTS5O/1id4LbFZadydh9yvbIjk5yZkPCus6WejlRXLspc+YAAAgAElEQVR3N6G7Y6s8o8OM0WHG8OBqEaeVocxtBaqzGduLaWFbJ3Vp8CPJ+u0mqzcbJCsB6aRi8Djl6V/HTI7yC58Z2aSkzBUr2zNOHszp7SWs3mgsL7gnxzlFWtm2wkASJh7JSkjY8Gzo8rBgfHzRrKDKNVWuGTyZs/+3CQLY+KRFby/BaGj2QibHeZ1fZ/DOibhFxWpy8mIDhHxuq32T45x0UhFE1lgjiCWjw5x8ViGloCo105Oc6Um+tKcH+7k5H5acPpwjfUlrLaS9EdUZeDnFTOEFdrs6mzG93YSkE9RGMHaG8RIGykxTFQWnD+c8+fOIzU9bbH7SZO1WAyHs8zU5sfNhRoMfWaEoPUlVasb180/t4qcrw2xQ0H80Z3yYkY5KGr2Q2/+hR7MbknRCW331Bd3tmLXbTRrd0B7HcWnz+55BSAhijzCxlbtFRqAfCnp7NqQ9bNiYhEbPZhmGTSteq8JGViwCt8vMHoeq0ExOcoZP5gRRi9ZqRHcvYe8XK+QzK56jlk/U8ujtNljZjgkSz87Jpe9m9ITjMufNrK7VB3tWbD/3GPdcOxyvg51XX3TAnHs/eR4yCOwM3O4NW7Xa2cNrNlGzGcXxEdmj+8zvf4eaTtDzq2+ULpBJQri2TrS1bZfVaqHznHLYJ3/8kOzxA4rjI6rJ+GJFTUpr/tHtgZR4zTZ+u43XbBGub2CUQhc5xekJXCXi/AB/pUu4tU20vUu4sYUucqrJmPzgKen331KcHFGeHGG0viCIvGYTv9NFZxkyigk3PfxWy7ZE7t5AVxXVeEQ1HLxUSFlHZWFbOAPfirj9p+RPH1EcHaBms7NlCGFF3Pom0WyKjCL8VhsRhASdLuHGJmo6wZQV5aDvRJzjhxEQsSLW6MoNEtHCw/9BFTibA1dRUVmnyfr2vKE+4YuFj+XiPqz33DYchaKipKSgMgUa9V6LOFVp8lm1NHJo9EKaa6FtXzPCfgjXbZWL65rjezPu/9uAR38YMj6yod/Pe3+rOvA4n1Wc3J9RpIrWasjqjYZt4ax/I+DkwYzRQcajPw4ZPE2tiDm3TK0MJtcc35vyl//nkPW7TdZuN2ishKzfbtpqiThrc9TaoCtTV7EKxkd5PWd3mcGTlL//12PGRxnjo5z2eogfe8uq1qJN02gbcVCkiv7j1BqgvMTBMqvbQO0sX0h3N6HRDWj0Vtj+0tRtq5r5qGT/b2O0svNqC4wxnD6aI/71hCJVeL5trbz7m56tNFW22rmIhdj/eszJgxmbn7YIE++Fc33GwPH3U6pcMXyaMj3Ol9vZ6IVsm7a98VHv+2Lb+o9TykxdCLHW2jAblhjmPP7jkCCS9rXUDdn5qsPG3ZadDawMRVoxPsoZH2ZI31r+r+41Lm2fH9pQ97VbTba/aLPxSXPZQtlej2itRcQtnyCSbH3exo+kfS0qQ5kr8pni8NsJ3/3r6bJaWswrDr6Z1IHchu0vrGD98l+s8FXK1O6Umlm/4N5/P2VlO2Hj0yZlqt7JEHjHZWxQTInmip7iKx+j6s90lxXncLw2xoBWF2N9wAqktXWivZsktz8hWFtH+AHlcEj28B7Z44fkh/uoyeSlFbgFfrtDdPO2rcBFEXo+Jz86IH/yiOzJI4rjY1SaXm6J1Ma6QxpD/uQxQnrEN27hJQ1b2dvapjw9RkYRau491+RERhHh1g7xjdt47TZGVZSnJ+QHT+2+HDxBpeklAQeg84JqPKQ4PECGESiFqNcfrK6jspTs8UOE59kq2jXElErnVKMh+YEVcOXpyeXIBGPQZUk16CMAv9lCRhHB6hp+e4Wg08XcuEU1HFgny2s9C9fHibiPAkEgItpylY5YIxLJSytwxixT3+rQbr08KVd1YLcy6oL4Egik8ZDCijdrT+3Vf5dLMScQFNicuMJkVFT1RcL7iyoN+awinZT1vFtEazW0VY/QzpSpUlNmmvmoYHpiXSH3/zpm8CRl1i+uvEbSylBkivFRzv7XE7TS3PxVl/Z6SNQK8AJBPq2YnuQcfjPh8Z9HnNyfMT25/KFttBUSw6dpLcps0PjWZy16N2yWXRh7CE9QFZpiXjEflKQju93jw/RKETc5sVb986E1NNn6rMX63RadTb8Of/YwxixFqapsm+F8VLxwHg4gn1kxEcYeUdOnzBUbn7Ror0c2bFsKqkLhRznDJylecNFu32gYHdjKlhd6hA2f1b2Ele0IP7TZZtmsYta3lcaDbybLOIbWWkg6qsjn1fPFh4HB07Q2OKnIpxUbn7TY+KRJsxcSt/2zVs9MLQV5MVekk+qCY6PR1iimzBRP/zpGVYbdrzoEX3nEHZ/2RmQDxVPFyYOKweP5Uqx2tmwW0HxYUhVnx1N6gqQT0N2N2f6yzc1fdS/tQlVX3fxAsvVpe/n9MrNVOKMMj/84Ovd9zenDOcVc4ccefiRZ2Yrp7iYIaQXirF8w7Rccfz/j4e+HrOzmqEojpCAdl8u23WexJkAV01N7Y2M+stVjx0+PRlGaAkV1oS3yqtk4g0ZRUZnyvb8x93o8/wh9vMfD8VrUNvr2BA5IOwcWrPSI9m6R3LxNtHvDCqTZjOL4kPn978ge3Eels+vnlQmB12oT7e4RrG8ggpBqMqY43Cd78pDicJ9qPLriwabOhisojg4wRuO1WkR7N2y+XBRbY5A4Rvi+vcY8LwSFsBW09Q3Cnd1lG2XZPyF7dJ9i/wnl6cnVh6gqUVVJIY9BCmQYEWxs4ndW8Fe6BOkcr9m0sQSwNCV5ETqdU54eUxwdUhwdouq2zEtUla22KYXf7tgKXhThr/Tw2h2iICB7cA9qJ8s3WY1zIu4DR9RSKhIxDdEilk08rmdiUlFSmpLUTJibCQUZpcmt6DJnd1nPTkziwi9ZB4HbLQjw8fGFNVGpTIWiYm4m7/Us3AJVarKprZSVmeLxn0YEsYdfCzghWBptlLmmTCsmJzZPK59UL7zJLYQ1klhUeob7KcMnGclKYAVi3UZYporJcc7kJF9GG1zFYjtGBxlaDTi5b6tcYeLh+baVQCsbCF5mVsxNTwum/Zx8esWHX93tMRsU7H89ZnyY8fjPI6LGmZDF2GrTQohM+zmzfsF88OLtXTDtFzz584jhfsbD3w8Jk8U8Vy1267bL8fHlY6BKTWEMh99MyCblsoV08fxUpaFMFbNBweggw2g797Vo+8yn1dV5evW+j48ytDL0H895+O/hMi9P1IGoWumzZfULZv2CbHJ53402jI9thXJ8mPH4TyP8UOIFsq6O2qrjrF8sA+Kj76ccfD2hzGz+34Kq0Aye2Krf8GnKd//t9FrHGkBXdnsXFb/l97XNBpwNCh7/+5DRfkbUtHOAi/m7MtP1zYfMCuhxyfQkt8e60FbsP+eGQDauuPc/+vQfzVGVfV2/aF7S8eNRmpw5E9omrT+nF60Ez0ehSM2EmRl9tBlxizPesx0oppa4i6xU12bquC5G10Hatejx2x2CtXXiG7dIbt3F7/UQQHl6Qvbwfi24DtDp/FpiBc5Ctb1GA7/VsW6LUtpWykGfajB4qaOl3ViDyjLEcIhOazfKOnZKhhF+s20Dx9VkuT9IuQz09ppt/GYL4QfoPKOaTCiOj1Dz69n0m7JETSZUs+nZvgth9y1p4jXb9Tzfy4+LyjLKfp+qFmgvXbeqKMcjvOMjgo0tu+rAR5LYfYsTlDGY8nrXO9fBibgPHFlHCoQiIRYtYnG53eo8i3K9wVCYnMzMGOkThuaY1EzJzAzFq90VF1jHskBERMSEIl7W9lIzfe+rcFC7DFaKYpYyeJy+/AGvgBDWQdBeTGfs/23C/t+uuCN0TYwGpe1c08vm0V5twSzz8Y5589koZ9l7L3DXugJdt02e3J9xcv9623ZeDL0UY41jpqev70BlNMxOC2anBUevuSxVGkYHVkg9+fP4tbcNAHMxC3H/65e/HifkHN97+XHPZxWP/jDk0R/exIY6XoeSAmMMuUkpKWynBc/LEDXLGeeZnjA1I0revBPb+4CHV7tzBhfk7uL4VJRAVQs5h+MaaG1Fh7HmH/5Kl/jmbeveeOMWeB5qOqU4OmT+7ddkTx6hswxTvYJY8K1Nv21/bOLFCWCFiU7n6CKvrfTjlyxI2LbBMq/bFs9mzEQQ4jVb1ihlPgdqi34pkYF1tfSaTbxG0+52llpXyXqW7+Xrtvl2Rim773WuLFiR6iVJ7bKZAS8/t+s8oxwOllW2l2GUQk3GFGFEks5tpq0fYDwfGcXIOEFXZf1cvpn3vxNxHzg+IQ3ZoSFa+Nd8uu2cWs5QHzHQx2RmSsac0hQ/SHAZjG3HMfZrTsb5k75rMXE4HI53D42momRihhypR7TECg3RIRDhMkJg8TO5SZmbMQNzzET3KcwLwnU/QBb9Jw3RZlVu0xDt5f+A7WyZ6gFTMyIzMwo+ruPj+OEYozGqwms2ifZuEm5uk9y6U8/A+ZSDAem9b8kePaDsn6KLHKNf7Wa7DAIrsKLYOj7WBN1VWr/4NfHNO1YcvSQjRgB4PsLziHb2EP7ZTR/hSRuQ7fsX58OkZytVSaMO8K6/HUY07n5Wt1ZW1xJSoo5ICLo9/Hbn7Pt1NU4EAUI+70bUZUxVofOsjhO4xrWvMTb0O52jn6n0CT+wbaVZhhaZE3GO6+GLgIZok4g2nghemvNjMFQmZ26mDMwxB/oBGmul+sOx4s1ameSui+SHIBZ/uKRkh8Px02AbADVTPUSZkkLmIAUJTQIRI7AtlJmZMdVDxqbPUB8zM2M+tg96gcDDIxFtNr2bdOXGhf/PTcoxAUrbM+HHJnIdr4ExGK3sjNnuDeKdPeKbd2xlSmuq0ZD5d9+QPXqAzrNrt1CeR/ghstFERhFIuRQZfreH3+295uYvnBxrEef5yxZLOAvL9pIGwveX6xZhSHznE+I7n7yBdQvwfKQf2P27zmOVssezKOE6jspao4sCnaaY8mK1TQS+bacMprzJ6zgn4j5wPDzbwkh0LTdKg2FmJvT1AXO9mFf7uE7GDofD4TijoiQzc9DHFCbFP1eJM3UlrjApuUlrcfLxnTNkHYweifiKllOH44chwwi/1cGLE8LVdbzOijXosAPztkK3s4cuC4rDg6sNOF6AkDZom2erVEqhq/LqgO5XYCkwn61qCYHwPGv8cU7cUc+PGfXqovRZVJbawG19PWfKxfpf2YhE6zMTmnMIIcGTIMUbvRfvRNwHjqz780MRXevEYtDMzYRTfUCxHGZ3OBwOx8eKokTVQm5kTrh8FWIu/fmx4S1FXOJEnOONIsIIr91BBgEyDK3QWogdIayF/86eNQIZjX6QiLPmIp5tNTwnpIyqXn2+7gp0nlsR94wwEkIgpIfwvOUMm32ARpc5Onv9qrXOUmsooi4LrCup8/kWucbXfQzPE4pCgOddaFV9EzgR94HjCZ9QRATixZW4xbxaSU5hMgqTonj9ux8Oh8Ph+FAwH7VQexGe8IhEozY1ebMXao6PG+F7SCJ0llGNR+gsRWUZfrNFsL5hc8nW1u081nyGkIJqPEZnr2DMpY2dedPnsmWNsa2a976jGg1e+21fjYeUpydUk/GF6ppZRCg8I+50UVi3zccPX3vdOs8oT08ohwOb9XYdpLAzdtI6zV5rE2pBeqGiCHa/rjFT+Ko4EfeBI/EIREzwknbKhflIYXJKMgpy3Ina4XA4HI6X4+ETkRCRIIWrxDneHMLzEdKjGg4pjg6sGOmfEu3s4TWaNgdtbR0QtmqlNKasXknEGaNtALe+OEJTDgfM/vonsicLIfXDrwuN1nbbjL7YnrkIM1fnAs2NwRQ56f17jH/331973RiDUfqV2ilthdCKuOu2QFpjFc8+5sLqta3Qaf1GL62diPtAOUtqs5bQtoT7olehoTJ2rqEyFU7AvX3GRxkPfjvg9MGMMPEZPE2ZDT7O7CWHw+F4l7HtlLYS59opHW8SU1WYsqQ8PbZVsYXtvdaIOCba2iFYXcdvtoh2dus2xAKjKjsLdo18N1NV1lWxKM5ETh0NsDA60UVphcibRmvrApmlZw6Uy3ULEBJTFW80X+06CM+34eSL+cOXISUiiGzG3nnzFmPs8c0WpjMu7NvxUmzkqBSeDd2+hiulqh2zXBvlu8FwP2M+KGyYtQRVGIrUPTcOh8PxruGJhYhzM3GON4spC1Q6Jz86IP3uG9RsagPAVWXntaoSL2ngd1aItncRno9KZ3aW7fQYdR0RVxbo+QxdZGch3FhrfC9pIJMGRk8xxZsXcUYpdJZZwXnexERKZBTjNVt2n39qEef7VpCF0bUcLYUQyCi0LptBcOH/TFmi0rSOK3AizvESFnW4hYB7mYgDaxVdUdaRAo63jSo0aeGMZRwOh+NdRdQ3TH0CAkJ8AoSbiXO8SRbzVEVhhUA901VNJ3B4YN0rmy2MVvitDn6nS7x309riC8iryjozviB6YFEp0mlqw71rIeLFMcHaBmo6IS/LawnCV949rdB5btc7n6PyDOkHCM8jWOkSbe1QHO5TzGdvfN0vQkaxjVgYDyn7Jy+vn3keXquNv7qKjGPbGqoUepE3l12OHnhdnIj7QFm0U15HvIGtxGmjUKZEv+HBS4fD4XA4PkSWAk6E+CLAw8PleTp+CnSWUVaVrRiFIcYYxA0fr9Um2ruFiGJUnlFNp7b98kUiTimMzlDzGWo6wWs0kZ5ExgnR1jY6nVOOhj/M+fKlO2LbP1U6R82m6NkMmk0b2t1bI967iZrP4Pjwza/7BcgkIVhbpxz0L4SQX4XwfPzOCuHGJl6jCYCuSnSW2d/5m3H5PI8TcR8oAoEU8lqtlAtsS6V2sQIOh8PhcFwDD49IJATEeLx5C3GH40q0xhQF1WhI/vSJDc2OEiLpIcOIYHWdeO8mpizJ959QVKWdOXte5psxYIytuB3sgx8QBaF1vlxdR+cZajZFSGln57LMtj6eX1Y9P7eIC5BhiAhD7E0NY1sm5/Pnz9Vpjc4yitNj/MMu4dYOfmfFtoju7KJmE3Q2R83nqHQOlbrYelnP0C2jCvw6jsGzrc2mKlHzOaa4vq+AjBOC1TXC9U3K9WMKIdDpvM66O1dN8zxkFBN0e4QbW4Sb23jNZn08p5T9U1s1rV4ho+6aOBH3wSJeScCBzYjTaGdp4nA4HA7HNfAIiETDhXw73ho6TcmP9gFqUw2PcH0DL0mIb962rX1a2XiCIn+h0Uk1HpE+vIfwfYJuD6/dxu/16uKyQTYaFAf7FP0T2x54blln4ilCRhF+p4vf7VqBZQzFyTGmfILOnz+yo/Oc4mDfLqPRsOtvtRFBgK4qRBCSH+5THDxFpSkmOyeKhET6PiIMkWGE12rhr3SRUWzF1HxGfvCU6lVFnO+jt22FEM+jONy3fz8nxqQfEHR7RNu7RLt7RNu7yCgCralGQ7LHD6hGwzceLwBOxH2wiPpP25t/3Urc4k8n4xwOh8PheBme8IlrV0pXhXO8DYyqMPOKcnBK9uTR0uY+3NjEa7YIpSQeDtDzOcXpMWX/dFl5exaVzimOj/AaLfzeKqHReM0WfmfFumGGkY016PZsi+BCFAlhRZTvI8MIEUb47Y59XFXambcspfCuvtFhytK2Li4EZNLEa7aQjSbh+gbS95FJA6/RtLN7WbqMJLAC0kcGtYhrNvE6Kwjftxlx/b7d71c5rpWNaRBBSLS7V88eNqlmU1sJNBoh5VnFbmOLcN22UuqyRM0nFKfHZE8fU41Hb7wKB07EfcBcz8zkMk7AORwOh8NxHXzORJyrxDneJmo+I3/yCKMUIoqQYYjX7uA1msQ379jWxq//SjUe2bZKdbkiposCo0fkB08RgY/OMuKbt/FbbSusGk3CjS10nlvxuFxGnanm2XZKa7cfIP3AhnwP+ogweuGNDqMq1HxGcXqMfPA9RlXEN+8Q7exaMVcbjSS37tjYharC1NesQth1I21Gm/B9RBBgioJycIqezxEvEJDPPZ6TMfnBU2QYEW5sE25skdz5xJqU1FEMi3lEGSfIRgMvaWKMQc1sG2VxsE/+9LE1o3EizvHqvIqQcwLO4XA4HI7r4gnbThkSI50rpeMtYsrSVnykxG+1kX5AhCDoreJ3ewjfpxqPqEZDqskYNR5dXoiyodvVsE8m6oBuKQjXNqxQiSL8uGuF2iLL7eJW2Dk5bdBVuRRbKrXzaC9sKTTGxilMJ+SH+1Yg1suXcYIXx7YK2KlbNAVcusbVGoy2Ri1VSZWlz48vuAZqNiE/eGoFZBzj14IYhJ3rE8KKxfpYmHr7q8mI4uiQ4uiA4vjg+cf5DeFEnMPhcDgcDscPwIZ8JwQiciLO8fYxBj2fkT16gCkLazASx4i6KhffuI2QkvT775hPJ1e2Veo8oxyc2nyz8YhgdZ1gfYOgu4rXbuM1GsggrPPQrJBaVsfKHF0UqMmEajqmOD6iOD6kPD25VtabUQo1HpOXpW3BPDwgWNsgXNuw6261bfUrDEFIK+iUsqKxLG0Uw3xGNR1TDvoUR4eUJ0eo2atFFKj5nOL4EHFyZI/B2vpyG2QUnQk4Y6zxS5pSDk4p+yfkB/sUh/tUk/ErrfNVcSLuA2UZMSB+WFPlx8ZZJMPCDkY+cwxF/VP2pxd/XvzoM+fmCu2/jDEYtP17bRtjPUCdA6jjMmL5CvTqr/XrUCzSHu3rEM7uP569Bs3ZL2POveY0GrV8HTqeRSyPu8SzfxPywvEWLzzmwOI9bs6/0/XyqzvuHx6L10sgQkJifBG6fLjX4Py7cPn12u/DxTtx8fmnL3z6mfoz8L3AnIV7l4MBxeE+OktRaWoNNZ7nLPkMuigoT48xqsJrdWzeW6NpQ6u1xkual8KoL21GLciKLKMajyiHfcLpGLU6xl9ZsUIqihBhuHxedFna0PA8x+Q55XhINRpQDuw8ms5STHWN50FrdJZaYTSfUZ6eEI5HqNnUOlaurCCjGBnFCGlFnKkqdFlgCrt+NZ0s2zjLQZ9qMn4lZ0q7P7YqqNOUajSyxjBpit/t4TWbyDCy61YKnaVU0wnlyRHFyfFStP4YLZTncSLuA2PxgefhucyaayKQ+AR11k9k834I8BdfCZDCqy/wxNnFtZBcvHA+L9aU/WUqKkoqU1BRUpqCgpTC5O7iznGBxUVhJBJCkRAQEYoIr34NesLDw69FxkLM2VsJ9lWnbNYjisqUVBSUJqcw+fI1p6jen4uZnwjPflrWDoO2ohIS4on6vY9Xi7rzbr/1MTf2va7Pv9cpKU1ev9czSpOj6p9xfDj4hEQiIaKBh49059nXwqvPuRExoYjtuXj5PvSveB+yPPfad2JVvw/t+bYwOYXJ7G+y9+Kca10kx7YCNp2QPrhnZ8+qimrQR18nZ8wYjNao2ZT0+79TnBwi/AAhPSuyipxyOLiyCncBrW2L4HiEKSvK0xNrXBLYMG7bSlivVmnQqs6cU+i8wBS5FWN5Vlvzv8INbGOWrZjF0SFqMkFE1rhEeDZKYNnOqQ1GK9v+qRS63k9rqJLZsPPnzAC+CHsDVaLLsq60zSkHfSteg8AKSKzzplFWROo0RWUpOk1/dAEHTsS9c4jlqeD8XafnVYEufs+2Bi/uX0lCkRCLJqFIkOJ6w5ye8AlERCQSElpvcreWaKOWlQHFq/UnvwkWJ4Dzd/s84RMSE4iYSMSEJIQiqi/m7FcPD0/UF9DnqiQIG81gzOLue71/Ri3FW1FfTJcmIxfZ/8/em3THkaxpes9nPseAgUPmzXurSlJJp6pP7/ocrfo36Hdro0UfaaGWjlq3VX2HnEhiiMlH+7QwM48ACUQESAwO0B8mEiRGDw8PN/um96XUFSVrv+A0/dkImcSnOAOGmJj4oJradoHsXsxGdFtTiYnk8C3OqnvewuN7qkX+Zv0n9q+/lIIZuUy3QYW/BmNiYkk+SyI4FEun7jXVqg8iqKh0Q6lrSl1SsqbBBRd257EOd1MjxMQ+cJU7rlVFFfe4qY98LOE14II39zrPKcyMQuZkku+c99Rv0LcbSP9b+9dF/1qnptHan/eSSjdUuqZiTePvBe44H9bsdd+jdI/RVxifQDnR3Qe7fkP9Ej1Ht10Y2+rPF3/EkDOhkBmFzPq1Yf/PFWJJSCUnZ/pk56bVmoaGoc28b9fgqF+DM79vcfe/3Cez0jtfh/jqt1tzW1p/H6i1oqGk3L3/aUyrbZ/Iesp7/b1QxW7Wzlz7nmqKX/ycsqT65e/wy9+/6eeEqpxdr7/+53wtYUavrmmvLp72d3vfOxdIrqDraD7+/rTHcIAxiBsQEbHPuPublV9+3Y0r8gty5E28o+2bfPa1PjCJSfxmJD34uw0REzkhMjEzOaPW+5Wdj0FRKl1TsmZhL1jqxZPeRMP5TSV3G2TcIpFKRkLmK5dxf/6jz87tNgO429oGYBAUQVAMEYqKEpNiJSfXjk622fqGmpa63+StdcFaF7568rgbvNhvWudyxql5T0K29+stnQs6dcVCL1jp4/Z3fyu7CYwTecNMzg5+T82Gja5Y6RVLe0XL3R46D4X4wC2VnInMKWRG7oO2mJREEiLc9Xjz9X1zg7nF6eK5175r8eq0oJBZn5lu/DVX9tfctd/gPf7j/RoiYk7NO07kja+Of3mthoapK/uBj/ZnGvbft1yXQkxCytScMpUTMpmSy4SYlNhn/XfvwSHl83k7l4aEkH9+ElLSUJUTVxEIQVuoBlzpRy7t70+SDEnJmJgTJjJnKifER6wD30pHw0ZXbHTBUi/Z6P1mUJ6TkFAJa3DcV8ATf134N//aTCQlISWXKTH7W9PAXc9zOScxGQ0VrT5FMK98tD/z0f7sE4XDCKpDx0Euk/7+l0pOQt6fa7cWR3tfh+GVGOFazo1/dSdkWKZMxCVKG2pardnokpIVa7tkrYuxM2HkOAZcZB+DuIEgfhvgqme5X0RcFtrdzJKdv/vP7QQd4XNuQ3E/k28AI4aCKYVMH98OaloAACAASURBVOkRuorHUi9Z6iWtNiz1ksfMDoaNbjgfrso4YSIzJpwwMXMmMvcb5+Soqs2dv2ff6d75XD+ppB0bXbLRBVf2IyBUrEF51MU2bDbncs6P5p8oxFVc5QuFKUerDSu94tp+orH14IM48ZuDuZzxPvoTb81PB79naS+50g9Ya9mwfOT6sPQbxVRyCpn7QOWciczJzfSzBMGxP1XcgDdA2FB+LtqlLiDf6IpL/R2xQsnqRvVkSEREzPzzGLLzn+PaRy2CcKUfaPYkn0LtLZWCQqacmfecmfd9NeV+yGeibLefc3DVqYo1pa7pupZrPj7JxjGRjJmccmZ+4Nz8QC6TR/+dtZZc209c6u9UXcmGoQZx256M3Xp4qMwmkpFK7jsxctK+KyPvk363tfbtI5KYqZww5eSRH9sWVaWl4UJ/9x0Hz0s40wkZE5kzkzNOzTvm5rzvern3zwzqhbv2DnesuSu9YqVXxHxArfXtzvVwq3Ijz8+AAzgYg7hBEPJHE5nzLvqJiZzsZN19y4HsDPuGz97yscFfcU+EIH6mKCeXKYVM+vY01zqZ+YpH2s8ZPeWxGQyZFO6Zj1KmesbKXrHUKza+MvcYG72O1rWXsKGhIiXD+Ha12wiVrYmZk9ocwTxR2+fXEeHanKZyerDKGGioWdlrSl0++uY6JaOQOVOZMzWnFDLrq8KJZPcO3u5DSGQgBqMRUzlhpdes7cK912vfAjfM5/Y2XGVtt8VZbj3+oCBYyIy5vGFuTsl99fMpKlSBl3NmXzcJia9+u7blMHu6W/1xidFopzNjt0J7/0Tp90yogKeSM5MzpnJCIXMKMyOj8OvQ43nshTU3l4lLZJqMmTlnaa9Y+YpxpZuxKjfy4hiDuAEQhEgmMuOt+YlT8+65D+lFEyoZYcE4MeecyFsKPyP4tRW3hzo2AJGIlIhEMibMAbiSj6Q259IKtZY0jzCE3eFavCrdUGtJJhMSDNwRxIoYMnJASb2Etn2E43ooDBG5TJnKycE2YvVDx41WrPXaVUoeaREP82+pFJyYN5yZ95zKWwoz6z//2Ii4DLjLgs9QVVcZl0tMF9Fo2QukDPX5/RynXhfma6I7gjghInFVT3nH2+gPnJq3/XPyFDj9gK163sjzEotrpz2Vt8zNGyYyIyZ91rXhNRM6QCZywhvzB87M+94g/bHZXXMzXDJ3ygkWy6V8IOpiQGi1eRHCJyMju4x3rJFXR2ibmsopb6OfXJsaE2JJnrTidl9ymYB5128uV3rV9+0/NK3WrHRBrCmRnBLtuRWIP6OpFEzlxM3ysXnwY/pWBOONd91MXCz751SCuE6NE6FotH6ENlZXS89lxkzOmMsZM+NaJxN5ugrQXaSSM+MMiYRMChZ6wcJ+oqH2Ae3L2NAYMX37udL0G7HYt6hP5ZQz8465vCGX6S0zhY9LQ8WaxaPMGo/cH9fSl5JI7hQQGfba8FIJwVshM07MW+ZyztSckEmxd815bEJlbiIzxPyBTJ2oysq6joSnEh8aGflWxiBu5NXh3XuYyglvzR99JWn4ZBRucTMRkUSIharbPE4QR8Nar8k0p5DZgbF8VynOpGAiLoNZ6bCCuLAox5KQiVONO1Td6uhocBLUFeWjCJqEAHgiM96bP3Fi3hwthPDYiAip5r3Qz8ycE3cptZZ0XkX2pWSlw3NvNEJ2WkJjEqZyyrn5gXPzIzM5fZbja3CiCg3Vizmnrxm3QmR9a/1zBhSvGTeDmjM357w3/8CJeUPkRUqeF5cqLZhRmBm5Tkg1xxBRd5sxiBt5MYx3rpFXhxO3bqjYsLbXYNT13B9ptfA5wbA7bGp3/w7Brcv0G3Yn533D/OFObnze/zUlZ8Y5jdRUpmRtr6mpHrRfv9GatV6T65QTbVEJj+Xm8W7V+AwZE2bmlNpuBlegCW2UE+YkpHcKtezSaMVSryh1jdWHb6MUDBOZM5dzTs1bpuaEVPIb18chdjf8VjtuGki7r9iK99xmkHvgGP1xRBqRkjE35yiWa/uRa/3k50Qsg3vCPyPMFUfENNR+/sYJ3Jyb95yYN6SSHX3ed/k86Pqa1tdGK9Z2Qe1bVp+C3nZCnUJfR0swjufI6+N74Wuui5G7CSqRU3PKubjXXyFTgtfqfc63E2Sx/R+9sfaGP0FFwKl3H6K/9v27hIwZZ1jpsKbzyqrLMZgbGTxjEDfy6nC3+rqX7480+Gt9/eB0byRK6/zEdOuZthu8OXlkV7n6WkJ2uDONCzCko9WHlUJuvZhHIfOjKn2Ca7ebccaSywc7jofCEJEzuVebYkPFSq8odfXgyoxhWzGVOe/NPzA35+RSEH11BU779s8gXR9U1/oAxqvWbiUXjt8ohev3RM4pjKsU1l1JS3OjPXGoBHVfIxGiQkzCROZ+/vAHZnL2jEGL+qTJgkafrhLn0k3tzn2rHUU5Rp6EICQ0lzPeRX9kJud9AHdf1K+9n3u8AdvAjZhYgkDX/at8zmIoRYy/c1qh0XoM4kYGzxjEDQL1YUfJwl4+yCIfAotYUlKygwPbqtYb1ja0Pmv70CiWtXWy+k4K/HE3M5WWLPQTsSZ7JcRtb2bd0vobd/BsC95uYRPUZwN9dnBrvh6MwyMi7yeUSNqbpwf1rWOqgSIut5ipE8HoaCi71YO2+1nc891oRaVrMi38YP/tx+eUDVNyvAUGcZ8VHQIRsavEyZyYdO9mIby+aq1YeUGTh1Yly2XKTE45Ne+dsueRFThV/cJbLJhI99egv17DI+nz0F5VL3hZBZNwN/uT7Q1utxW5GESYmlPe8BOxTbi2n6goGXI1LnhjJprSSU4uE87Ne07lHZnkX2TnXXXdhTnBAL2l8RUrdy9wr/FQeVe2tfWtOvBWtXDrIeY8rlzLWAi9Q9tuS8tTncdGa1YssNZS6orECxOFykVvviK7H9t9L71lTSYFGZOjqhwvgY6Wja6I9ZLOtqRfIW0fAoiwxh66t1u1vV/Z0/mTKRtd9dfyY7NtH5878Sbzjkwmfl25u/q72+1S6Yaasvew7NfivsV7u+5sr2N3/9ve+5zydErh771m7/MT1tyUnJk5p6OjpkSte85G1cqRoTIGcQMgKMJVuuFaP7p2tW8kZOYnzHsfuUPHUGtFyYpS11S6/uZjuP13lNS6oaF89CWl9ucz1wmW93uOy/pN84a1Lil16WT4/WJSa9kHczcfjWO7LLmFICIiETdrNvMKaGJOSUjvVQ1MJeeEt9RScSUfHnTvF5zBaq0odU2uboN2dwXRWTaIGBLJiUncpncgQZwR305pToiPqsQpDSUre02l6wcPRoPS7EmvfJdwTGVMUVqtKdmwsBcs9IKNLtjYZZ+B3rZRhmZKCE29ghBL4k10Z0zl1L35IPwQgiHGOHNoE2MwlLqm0Yrnd5m6G/HCJolkKNbPwf3I3FcAbkOxtNqwYcVaF97Uft0Hzy5g7m4kK0JgE7nf5vzEyHvD8MI4OxPIiDGuhqBu415TPulmsKGmUzf7uq2+fdZ+Jk5G3/Sy+v79ZwmBE3lLanK+psoxRNzzvgQLlawPiiDdRrCoKZgf1elhsf0a49bApxG5cXY1T9MSHa6ZiZ9Hn8uZt/A4fO+zuIBppVcs9BNrXbK2iz6JtV1xP197g0+cT+R4D7qpnDA3b7zo0XFrb+IVStVYSl3SmNr7yY1B3MgwGYO4AaCEG3zJ0l5QyrcbpIYNhhg3l8MBzyxF++H7lb16NFPnkFWrn6AS19JQ6qYPSkPm1NLRaN1n4N37ipqK2kvvbz/e0NHcq+okmL71o7U1jVSsdclMTilkTiLJUYGG8b46uRRkMqXWipb6QVv/Wmo2uiDTwldt7rhOxG9gNSIjZ2Lm3jj6ca2xDxEyvwmZrxYUB1tZW21otXaKlFTeXP1hrkXnTZgxkzNm5oxcps6H8I4KRvi9Vl0Sp/QtwBtdsNEVG7/hqyiPlr/u1GWtW2mopWIjKzayZKJzb5o9IXhPfk6oyMWagDi/vRPzFixs9GGrwQ+JISKRlAlzJsyYBAU834Gwe56DGql7cybcFWtqdfeBUIkPBug3z7tg1PQBT0RJzJqUFWvNeqGYYAgdKnkbXT5D1TpMTn4ZOvaBnPpKhkaIhLkig6jpxWJiTUijAot9RCevpyX4ZXa0rgVZ7//IUsnJmLiuBJnBgVZp9VWmtV6z0SWlPo0ReqlLtE/+PC6ZFN7A283AxZLubd911cnK27ws/Jvr1gk2OKED4TDutemSi24qvrIla10wlZO+1T7eMzMt4pozMwpOzDusVT/O0PjX0nATWSNfj1qLtg3V3/7C9X/638C4tbH663+jvb5Gm9r5xAyQMYgbBG6pr6lotUH027OdiaTORFNzOu0OJsJcBaBioyuWesmV/fTNx3D7b9m2MT02bnat9Bu1Vb/Zb6lZ6pUPVq/6zWmr7WfCEV93rK6yV9Op82NbckUhU96YP3BulAnzI4M4g5CQepPiWkrfSvdwG+mWhpUuyHTCVE8OXifOf69gJudYtZSsn3VeKoRwaWhblYxDymeNVmx0SaWbfrP+UGReiW1mzpjICSmHjwfcpnKpl1zaDyz0gpW9dNcvWzGTY3Hft/GPc+E80ozzzntjfiIWZ2ps9hyXnzBjInPOzQ8AtNYFv0PEtbVlJOKkwnMmpF8kJFyr6tK6TP/CXrCyV7SEGcObr/ndiufuzwjPSUeLaI0grNVs61vi7DhSMn+uIx/EDQf3GF2Q2reJqnvv/wVApAmxxMz1fDCt0w9BR0fFilo3iH6d3UQhU1ppyc3kqNen+pGJlV6ztJesdfEVR35/QjLiKcj9Ondq3va+ovtwge2apV7yqfuNS/vbjdGF+/m2uVdl4yvspa5ZcEkiGW/Nj1jzB6ZHrr0pOWfmPYKw0aVrSd1RvR15ZXQd1lrWf/4vVD//rf+wrWtsWaJdOwZxI4cJLW4PgagQEdPp8VUGi8WqGyAeasb9fritSqlrLu0HNrIiIqal8Tdmlw2tfLbvITcp201S1/fzx5qABQzEmvZtS3cR+vRjde1xpeQPLu3faUPJygeyDVYtt6mHyc7mLvMm6qWsQIXnzE46VUpnKZBIetTMYRA02SovfjuhSS2XGafmXW82fnhOpuurb1f2A1f2g7su+ZZ25t2rzwXq1lo6aTHEgDKVUyac9Mf9xfPdz4hkTDmllZq1LPrZvKFt6EMVzmBIvfeXIQL1CSoaXzlecG0/sdAL1zrN6p4bRYeicNd3qbj2SUmJ1LUrDnemMDyGPWdA9RXOBG2rlF9LS+JDhuPOjaJYtXTa+vnz17DGOiISEi8mNJNTNwdHfGfFy2pHg+uGuLIfuNKPrnWc5TcHSp+vva3WXNnEPdumcwJQmu49PiMu4RvmrCtZs9HVk7XAjjwDqtjNGrt5+FGix2QM4kZePRtdofaXXmhgK1bS7cwZPeYGy20ir+0nGqnczJJOvT/R4aAjKH2lUhDJ1YPuBVs/4F/oikYbrNi97S9BpXJulIV+4nlDOIiJKWRG0c+dHca1LV9SPuAsXFCInMicM/mhb1k8REvDQj9xaX/n2n5ipVfeZPvhcK3SlbMosEqpK34w/0AeTbxi3N3XoPNZm9NKzUQuqMW1IbZDC+LImHvxhGC3IP7qDG1sF/Y3Lu3v/fzbrsrdw6JeJKXrg+TXFwSNjGxJJWMiJ0zlhFymBzsQLB0bXbLQCz7ZX7m0v9M+SqVL6XCWAZV1m/NEMgqZkR9QywyG8FM5pZHKyU3pGMSNDIsxiBt59YTK21Zn7uk3oKGVBlVW9sqpF3J2lCqakcjN2niFy4c+rs7PKFa6ptZNr+Z1FzGuMphKTkzaq3g+B5HEFD5bGu0RJ9iqETpxnbUGz66HuRZSst5kPJeJk6ves0Gw2nnD9QULe8G1/eTNoB8nOx9afDeqtNqQy5TMTvoA+C6C4EUuE2ZyRiMVnT/2IeFUYbevjbAZbLVhoysW9oIr+4GFfvIqkY97/KEaMDLyPZBRcCJvmMjJ3o4IVbsz/3/Jpf7OUq+oeNgOk89+qx+XqFnqJYnNwCiZ5Oxqzu4S/h1LwlROaEzFuluwQcaWypFBMQZxI6+eMMXi/v68N+COjpVeE9mE2CTMOD34PRGRr8TlDx7EQWg3q9mwJGfSK4zdhSEmwXhVvgnomvqJ1M8+J8LZRzhrgcPCAkE62gVMD+fZlcmEU/OOQmZH+SE5ifNlr0D5GBW4u36vAgs/8/rW/OSrhvuvq1hSZnJOKw0bWT+ZKMO3UmvFpf2NC/u7y8brZqyKjYw8KK47w7WRz/euURb1iZU1V/qRC/vbk87ZrvSazrbEJMyjNz5Zefe9OiJhYma0tuZKPmDU+ETwGMiNDIMxiBv5LhjKTddiKXWNsc5YOcygAXf35zvpDmKSo1r0voZQFcrUiULcta6FWSlVIZWcicxQr/b5lOdYvD7gVtAkP6xKSeMVCUsnv/5AXoiCIZcJJ/KGXCZ721HDOWpoWHop7ceswN32+zta1rrEWkshU054Q6zJXh871yo6ozElmeasNX6CNuSvZ7fSea0uUK518yj+lyMj3ysRsffodJYmh7o4LB2lrljpFWu7ePJkUKMVnTZsxKn+Cq6zhDtn44xXPp6QSUEiufsZ431kZCC8DtOXkZEXQjAX3uiSmqpX4dqH855zi+WhCs/X0qmrDDlfnuNazVLJmZkzMpk82nHdhQvgCjKZ9LOF+wNcdUqh9vJBBU2CpEkqBXNzTibFwUBbsTRa9W2U9ZPPWbgZOWdl4OS8D1UlnYS/28yEVtpD3pPPSUvN0l5ybT+xttdUunmSSufIyPdEIikTMyeTqWujJGZfZatVl7y6sh+pH7WF8nb8RDyVbnbErfYnosK8c2iXd753IyPDYAziRkaemI6W2nvjtFpjD2T1xMuWGzncpvctx1R6K4ZGa6dqqvsDnZSMqZySSXFQSvqhCdnfnIlr7pS7q0jgGj1rLfuWuoeqIEU7HnW538jse4qsOvnrSjdsvCfSc8yXuUlIZymysldUWu4P4sR5hqVesS343w2NMPfYqLMRWeqF9wNrBqeoOTLy0knIvJhJ4WxLxNx6H1Z1ypyuOn7NSi9pnsWuRH0i9bi56CBMFElMRkHBlFiGd98b+X4Zg7iRkWdBvR3pmvZgELdV3Hu8o7HeALv0ZufVQbuLVHInJ03h5yCerhoXiZOznpjDs3AOpdKShV74IfpvD+JcBS5jauZ9G9Gh56jzaqAbXVBr1XuOPReVbrjWT5S6Ouo4nJDMjKkc57f0HDg1zpqVXrLSq1cl5T4yMiRSyZlK6Ma4+96nfiK51YpS172lzXPRaM1al9QcJ27lrEtcsi46UgV5ZOQpGIO4kZFnIHhX1VrS6YH+egER86iBXJiVanAqla7NZP9xOZXKKakUxJIeZZfwUMRe0CSXKdGezGjIADtBkw1ru6DRhxI0ERJyJjLvJbVD5vYuuh2Pwpb62atDTiXuisrbLagqusfU1PTzL3OSAW5mVC2dNtS6Ya1LNroe51dGRh6JUIlLJd9733PquA01FRUbZ3nyjO3NwTeyPnItEO8/GfzvRkaGwhjEjYw8E0F84djFTPow4fFetv1snF4fbHcJFULnEXR4qP1hcL8zkZTiyLa+ltrbJzy0oIl4z6E5iWRH1SE7bb3p9Opw8P4EONuBpTO818Mm3gZDRkEm072WDs+FsxPx5vVaD1p8ZWTkJePuf6lrIyfbb6ni59BKXQ/ivtf5lnaXSDt8f3Cecb4SN7ZTjgyI8WocGXkmLNZtnOWwsEn/dzEYFewj+dVYL32fasGEef87vvDREek/mpIxkXlfWeQRq0vGi7wExbBDgWOYj9ro0vuDPVxrnSAkpDvD7vsy0e487s4eDqFC1NL0JrYNDREJMe65vQ0ncJKTaeHVUoflm9T6ILnUNc04Bzcy8ii4VJpTTc6kIJaEffe/jo5aN1S6HoTFhzueklabfo4WvlznAoIhkYyUYqzEjQyKsRI3MvJMuBbG7h5KifLZ+4eno2PDirUe33aYSM7UhNm4x72lRH0b5YSYGHPgXCiWStcs9PJRDGVdJnriNzGHCcpotZYDUkvUvlrZaLVXrU2CVpskxJKSPIIB/bdgafvza3Uo53dk5HXhWuldG31oIt+HqqXRmoZ6EEGca7Bvjq7U9/c94iOUkEdGno7xShwZeRbcqLf1c0hHCW1I/79Hw7W9rNngvMtUu70zUuACGadQNnn0DX0sO7Nw3jfvtg2E7vyp2LC0F75K+HAIQkxKJpPD4irq5vM6bam1pH7mmZDPCTMizYH2IhEhkqivhiY87SzkIboQxFEO6vyOjLwmYkmcvQvpUbPAFktD7X3anv91eTOBekQQt3PfcyrRd3uBjow8JWMQNzLyrAynFQ22oWWrDaWuWevqYAti7FsKQ1vNYwZyMSkTmVMcEDRxaD+LsdLrBw3iQmY2kthZHBx4zNYP9ne4ubMhtSACdNq5eUFtOOaadHOJiW9nHU57UacdNaWvIo+tlCMjD49raU8lP3o+TP2a4toXh/O6DOvdfY7J3/nHatzIIBjO6jsy8t0RevGHtaFXlM4HcRtdemPt/M6vjyUhJnEqlaQYL9v8GIFK4q0FCpntrwApWJ9tDUHcQ24ewkxIROy84Q6g3pltK2QzrOfc0rksubTHtReJq0I6o/XNYB6O9bMuri10OJvFkZHXgoD3jMx9N8ThilRQP3ZJrLtnz56D0LFxzBEFMS9DNFb6RwbBGMSNjIx8QUdHqStWek0mxVHfk5AxMXM668RRHlK4IyydQdAkkf2zWIp1lgI7hq4PF1SKM/mW5OhWQhck1V4xcXjBRTD/bmmOltyOxAWwRoeTkVYvFtSNqpQjI4+G8Ym9YytxqeS8MX+gkBkt9SAUKgFOzJveHuYoxK9FEiEDuu+NfL+MQdzIyMgXWDo2uiKxV8zl7KjvcbNxcxpxXnMPmac0GF/ty8llQsrdlUFw7YuVbljZa2/o+nAbesGZXseSYuTYIM76IO64IOmp6bxCpdtcHTfoH5P4yutwZuKssxT2Gf/hneeRkZfPViHYJbEO17BSct6YH4EfGUzZHuAIUZbPv970nq3DqSaOfL+MQdzIyBe4Sotrl3NzT8b/24jZGeTeDnTfGO6WcHvffjb8K/zfYChkzkTm5DLlsQVL7osLOkpKVtRUtNr0j/9zwuNzxq+nlLJhwcWDHk9MysTMevEUZ3FwN4pS6pqlXlFr9aDHsp2KSI7O4KpvqByqb5l6vbbjj0/6dtIhzIaEY7454zK88zwy8hoICrXHJnBE7hssDRO3grt2yhf/eIwgZt+9W1GroDreSgfMGMSNjHxG70UmGQkZqWQkXk49BHQuyItdk5/4AA/jWy12g7ov34fP737P0HDtiCURMY2WvjVS9loIJJIy4YSVXLsK1QPe+BNJmciJU6U8ovplsT6Iu3wEVUr6AOZYSwX1NaJugPNwAFbdzMqxrZ79OZBhBHEB9WHcEAPlkZHXghHjLV6G89p/KsLq/dKDOIkMkuwJAayFzqLWgh0DuaEyBnEj3yXbOkJKLAmJpO7vJMQSE5E4VxhJiCTx/jBx3w8fgq/tDX2nQndLJW5bhdut1g07O+kETpw59dpeU5gZ8Z5bRlAsS6UgJfcy+g/T1pYQbAym+2fh1NWUWq2odUOpazqab/79N3HPW3SfbKziq1zDDDDuX8FybUWR+qroAB5SOPYhnt+RkdfCthoV+zXve2LIK/Y9iAzF//QTk3/5I3d1AXXXa+oPVzQfF7SfltjyodfRJyYymDwhnhWkP54Sv5nf+aXV3z5R/fUDtm6hG94M+y5jEDfy3RECr0QyCpkxkRmFl63PZUoqeS+kcbMJ8mbQdfN2fvMzN3/f7R9/CWwrWldEmvjWz9sxRCQYUnJSyYnZ0KjTiPxW4iO96HwIR0NFxYZK14+yqe+H2+/RTqmqqNpBBDxfsq1gHRfC7Z6D57+u73f0IyMj30JQaBxSFf4pef473rchkWH67/+Rd//L/wx3jCZUf/nA6j//lfX/83fspn7xQZxEhnhekP3pLfP/8M9M/vVPd37t1f/6f9FeLNFu7SqRA15WxiBu5LsgGDO7SlFOJgUZhXsf3ihIJCOWdPBVsqcimH+v9IqJ3p25gu3cQyLON66motMgqf91hNbVUOGLvbns3cdrqXTNWheP6hV2/zrqbpgxvBUhzOwdf2yfV5mHwDDP7cjI68NV4t1s8lBe/yNHIxBNMpJ3J3fOl3eLDdEsw6Qx7J2de0GoD+bOp2R/fHPnlyXvT4hOJ9i6pWst6HCrcWMQN/LqCcbMuUyYyxvm5oyZOSen2Jlnu9kiOeJwlbgVRiNOeX/U9ySkTM0JtXXCKIfMwvcREZP5oNupocV7nx9L5yqH9ormwQVNdpGdt8N8jansU9Ifnx4vCDKsAG5kZOSpcDOx4+t/5AWhijYd2nZIFBFNszu/NJ5PSM5m2FWFXVdDjuHGIG7k9RKm3nIpyGXGVE6YyilTc0IhMxLJnmwjerNCoN7wFG42Ww5vQVSUhoZKN9S6odaqV+y8i1gSJswpWXEt8Te1Iriq3gkZEyK5XR0zHCdsrRFWek3NYwZxX4fu/H9YaPjvnoyZ+JGR74/7ve6tWm/7EeZWXyaNlnTa+oTcS30U3ylWsU2LrRpQReK7xzLMJCU+ndBcLMAMe30bg7iRV0tMQiYFZ+Z9bzSaeCGTQxWdx0J3/uxKnQx3I6xY7yFWa0mlK1IpiPbcOmISCmZOSZIY99i+bsFLyJjJ6cFZuECnrhK30msa/foK4GMx1Gf5y5TCyMjIyG3IZ+8PY+motfRdGS83/Gm0pKEeg7gXiFpFqwZbNugBsRKTJcSnE6IiQ4wZ9DM9BnEjr47IK0lOzSkzOePMvONU3vaCJYc8xgKq24Br6/FlsRrmm+yNoMzd1HffqBHzVgAAIABJREFU494rN/+NRYFUMlLJXWBJOtgddHj8bjZugWDIpLjz600/w+YFTjTZycLej1QyJuaETIq9ctbqJfIbKmpc1fBbZvEOoztvx7AbrA/viRZAxHDf49N7nYORkZGXj372/jAtDSu9YqPLwSr0HkOrDWtd0Goz2Nb4kTtQxbYd2rRo27lATgS5pdIWgjgzScdK3MjIU5NKRiFzzs0PnJsfKZj2rZP3Jagd1lrSaEVDQ0tDqzUdDZ12PsCz/XvVrs/UbWehQmDnbvyC4dS85UTeMJVTYkkf+Cw8LBZLyYalvSQ1d/eSQ5CfFhIyCiZUMqHUFe29Fj0vkkLGlDmZFAcETVymt9QVtVZ0PuB+PLbB+DEENcfhSnL7Z+2OdtXbGAO4kZHvld3E5WEarbiyH7jSD3Taer/Ml4diabSmZQziXiSqaGvRpsM2HSY2YL7s8OkrcZPs1iBvSIxB3MirIZhpFzLjVN5xIm+ZyzmxJP3nb6OvkalrHezo6LShpaXVhpaaWkvfDtLQUNNqQ0dNR4fVrZGzq9R1O6Gb7UO53QUvzOtlUUHG5PFPzjeiKJVuWHHFjFO/gG3d8Hb5XKWy1A0NFe09/Nqi4OLXi5qkBwVNKh5flTJwf6ES2VaBB+KrtsuuIf1xhDPwcrPqIyMjX4ei2CCCdMQtw92fN6ztNY1fN0dGnhwFtXZbkZOE28b7JYuJTgpMPlbiRkaejIiYmJSZnPMu+snPZB2eowo4ZcMVG12x1kUfENRU2BsVN7sTrN1so7z5b268f8koSq0lK4RKSyz24KY/IqGQOaVxi/d9TkFCSm6mro3yCC+yjo6NLlnp9TepYR6LxWL1+ABGEIwIosOUat6a1h8XxrkrfPsaGBkZ+X64aUmylenah6v0R6AGxiBu5LlQ0LbDVg0SGW4Lg0wSE01zTJYcPX7zXIxB3MirISWnkBkzOWUm58TiRDXurMCpW4A61zBJpSUrvWJlr1jqpRfHqGieICgYPuqCI1VflayISYhJ7ly/Y4kpzIzSrtzX3YNEMqbiZuEiojvb/NxzCJ22ffD9+IIm6ptn2+ODOB/AmYFaWIj3fQoG94fRvgKtQ9ZfHhkZeVD6BI5Pah5DsPlxBuHDu/+NfD+oqpuJq1s0v31fInGEyVMkje40Qx8KYxA38ioQhIk54a35iak59UqG+198IZtY6por/cDCfmKjK0pd01LRaP3I4hgvi9BAV7FhpVcUzPpW1dtw9g4TMpkQS4rRyC/6hwOfVHKmcnpwFi5sJ1pqSl1T2hUd7f0f3D1QwKoL/a0ed30IQiTxUVXF50DEeMXW4yqF4bXT6dcJ1oyMjLxcnGVA088FH76jiR8hGOb9b+Q7wgdxtu2I7O17EYkMJo2RKBqiDtkNxiBu5BXgFoiJzDg3P5BJvrfisespVvvq24X9lUv7ez/39pTH/pIWNYul1g1Le0VkYgpmdx6/kYjEK1kmkhGT0NIcFRgnkjGRE1IpDszCKa021FpR64aKksdvXQ1yN80XM2F3Hau7QuNns7Y4hMEQkRAdGWRqX8Fu7yHvMjIy8hqwdLTaYOXYJJbp58CHeP8b+b7QzombqL09ASmRIEmMxMd2pjwfwxzQGBm5B24SLutFMKIjW/dq3XChv/LB/o2lvaTW6kkrb2EOaeg3iZs4gZOFXlDphkMBkyDeN25KIbO9/nLh6104kXlfv/2qoh2ujXKjCxptDh7PQ+ACmJb2HpVag/FWEsnR1a6nxBCTSEZEcvRUXKfNvc7ByMjIa8AlsbYCJYfvuQZDIhmJZBg5fk59ZOTBUaCzaNvBHZU4jMEkkZuZG/j2bHi7iZGReyCIU3mUgpSClJxYDgyjqmLVqWVd2t+5sL867xdv4vmUx34/RcDnJwicuKC39JYK2s+m7RKMzCNiCplRyOzgbJwhIpKEVHJymbh2zT3PpZuFW7K2iycRNAmESpxrqXTnYN9exhARS0oiwwziIiJSMv/8HFeJa2m98e0YxI2MfE902tJoRafHta4bMSSSuiDuHmJjIyMPjipq1QVyd7VTGkF8EDd0YZPh7SZGRu6BIKRS+Na7/f5lgZbGKRnaa0pdU+vzSB67OaSXNyPQ0lDpmooNNdXBGbRIYgpxlbh9M3QQBE3mpL4Cd1iVsmGjC1begPWpCPOBwWDcHjgHoSIZkQzyOTcSkUhKJMe1O1lVWq1ptDp6LnBkZOR14NaAjZ+LO4zrRMhIJbuXYvTIyKNwR+K5JzKunTIa/rU6zsSNvHCEVHIm5oTkSMNs14K3ZK3XXsTk6dUnBbewGYkx9zBYHgIdbW+AXmvpVQ3vDkycwMmMiWz2troGc28XkOd7q5ThBtz653KjT1uJA9dU5ObxSkTy/Y9NgjZb4sRN1MCAPNYi304ZH2inDOfdOSo2vbH6yMjI94HiKnE1G1ptCfY6sG8mOCKVjFTzsRI3MgxU3dstiDFIIr6dclgJ1895WbvHkZHPcO16EQnp0YtD6+XoS10f3Q7y8Lg2w5QM80JzKbWWLPWSSjd7gxETFnApfCb27pbC1FsLpJLtrQdZLA1OzKSm9G19T6+S2GhFqSs/j3eYSCIyJmRMBrSZEWJJyGVKIvtN1WFH1kVrX4Ucg7iRke8HpaXpE6DHJKIMpp9Zj+XYuduRkUdAAGOQOELMyw+BXv4jGPnuify80bGb4o7Wm3qvH12Ofh9RLyYxlM38/QizcZWu98rMh1aarUpl7AVdPkd6a4GEnH2zWZbOGbHrxiuK1k8uda/eO2+jq6OrgGF+0wnwDCF4F9/qmbogjmzvzF7fRqotLWMQNzLyPdJSU7Km0WODuIiUnNQLj720WfCR14Q49cnYgHn51+AYxI28cASRyAcGxwVDoZJwH7Pmh8Q4LUAndEE6kM38/anZrcRZ31Rzi8CJCCKuYprLhMLMvxA42VZUvSqlHFCl1Ia1LljrkvaZAnEn8lKx9qbw+5RNdkVecplSyHQQz3tCSsGMVPKtqfqedc3SUemGUldPbMUxMjIsdOf/9+Plhy/BJ7Lx/pyN1ne2pkFYA9y6V8iUiXy5BoyMPAlGMFlKNMmR5PY9o1qLbVq0s3uv6yEwBnEjL5owWxbdY7ZMVem0xT6LUbF3DJME9+flqnW5dsorKjZHeYUZcebfk1sEToK1QCq5txbYP9/YEoK4Bd0TCprcRKmpWOuChvqo7ZwTeZmQy5RInj+IC5uqlHzvXGMg+ASWrJ/xvI+MDAA/UuMSV8dt9LZSTS87jAsV+VYrKl3RanUwIdrbzcjUrwHHzbCPjDwkYgSTJ0TTHJPcsQZbdT5y3dOPaNyXMYgbefHcfzkMNaOnf4EahEwmTOWMRPK+SjV0GdvbcG11Ttij1CW1VnsVnyIiciZfWA0EQZPCt/MZvKzvLackPG8tNWtd+lbG56vEtVpT6opKN85yQLsD5yBU4makXm77OfPymRTMzRtymbjzfkAR1NKyYeUD1zGIG/l+CdWoY9cRwVWjjAxPnfZr6bsx2D8XHe4rsSRM5IS5nJMeaJkfGXlQIoOZZMSnU6JpjmSxEy65Be1CJW7/ej4ExiBu5LvD5U31XhnUh0Iw5DJhJmekkj/p735oQltqrSVrXVJreWA2LiKTCQUzohuVOCGRnELmXtDkkLCG0mjjVSmXzzrXGNqJai2PMr4OQdzEzEjIiYif1Tcuk4ITc04uk6M2lp12lLpyvnz69KquIyNDwdWibN9IfgxOx/cVBXFe3Ko8MBcdiIiZmjkzc+4ViIUxkBt5CiQyxPOc+GxKNM0waQL7gri6RVv71FvEezMGcSMvHotuDZePQMS17hmJeMoFRAgzATNOzBsyiif73Y9JrSUrvaLS9V6FSIMhES9w4tVEQ4Y2lYyJzPs2yrsqQp22VLqhYk3jPeqeo6K6Rfs5sYVeeJGX/bMhkbjZv5k5ZWbODraOPgYxSV8Vnch8Z0N1O1a9nQIlpa5dO+UzBs8jI89NX4nT4ytxMTEJ2SDmYR+CWqveb7U5ohPBrYGu62Imp74id5y/68jIt2DylOyPbyn+xz8Qn02dofcdHVBat3SLDbbcP+s5BF7HnWTku0UBVUtHd7TEvCBEEhPpU1ZBxAcxKROZc2LevJrB7oaKlb2iMNO9AZX0hq95r1LZ0rogjpypnJAcMGxvadjYFaVd02rzzAGcQ4FS11zZj0QmppAZ+/NjQiIZc3lDJy2t1FS6eaKj9b/fb6QKmVHI3G8q9/jD9b5wLog7pEg6MvLaCUGcC+T0YD5QMESSkkjmEojD3hseRUNFp623Wal7+4C7/eJcIBvauDtaLq1Sa/nERz7yvREVKfl/957Jv/yJ5Hy292tt1dBdrbHrCrXDXufGIG7khaN0dK6VTY6TOncy7zmp5F6e/vGJiZnKCXPzxgcr6bO20T0kjdasWVDqxgnGYP38x82FXMQrNKrzx0ulAN+CmUpOYWYHDdtbr0q50eWAhDXUVeLsBYVMmXOOqJNq+eIc+M1NTMJEZnSm8epuFQ31o1e3XCY8ZmpOOJV3TOWEiMOiQEFIZqVX1FqOtgIj3z1KR6sNnZ+LO2x4LaT97K/zY3wOdeSHJDSTlrpmqRcATGXOXUksdz8UIk2YyglqXEt+a1sadX6fQyG0egrbEYxXEXl/L3g/uGiSEZ/PyP/7H8j/hx/Jfjonmu4fZbFlTXO5pFtVYIf9nI9B3MiLx9cIjt5YRn42K5cJa1k8+n05VKBOzTvemp+YysmLVaS8jZYaqx0Va1oaEjrvfXf7ZsaIIZWCQmZe4awhFWctcKjNyAmaXLPRFd2AAokaF8DO9YxGa4y4abc7zwERuUzdBsg409ylvXrkIE68N2HOXN7wPvoTqRRHzee02rCwFyzsBQ1j1nxkxAUwDR2N/7vdm5gLhteWzlXjiPrve8mEIO7S/o4xEfkRHpgRUX+/76TDGmVhPzmrgkEgvZedwaA+WfzSg+7vCjFIEhG/mTH99//I9F//RPHPP5K8nSHx/v1Xt6lpPi3pVuVYiRsZeUycQmBDpa697pgRt0gSJsyoZEPKlVfWsg9+g3ZD7Am5TJjKKSfylpmcEUv6agbbgX6837XarYhJEMmJ7srG+s1MIVNabTDigtx958Wq9YImFRtd+vm74QRxoZ13o0sW+gnljInM7zwHRgwGQ0bBXM5RsYgRUGi1fnAPNuPCNyYyZyqnzM05hcwPJhOsdnR0lKxY6RUrvaYZTAV0ZOT50LC1V5dEbLUlJkbkjteU+CQKbv53bs7cfKmun/bAH4FaNyztJZkUTPUkGOncWeF3nnEuSJqZM7CKMQaxQq0VDdWjrMm3I73gTERMJAkxSd8V0mhNQ+W7dp5pQy/0/mYmT75ZzVqyBDPZP7ogcYTJU+KTCcmb2XB3LOFcGHFzblGEJBGmSIlmBdkfzij+5Y/k//Se5M0ck9/e7bM7y9mtSupfL2mv14O3GRiDuJEXjXqvrvuYDztxkTm1VCxk4vr5qdEHDArEB3CFzHhjfuDUvGMqp15A4nW0UX5OoxUrve6NzO8KDwzGtxXNaKX11aEUs+e8bJUwK6cGSTmoSlzY0q11wcfuF4hwXnAHvismYW7O3eO3EWINSy5dQuKBcJXglFymnJsfOTc/9mbjh5IJHS2lbljbBWu9phxYBXRk5Llx96UNDYVPztz9qg8zYTM5o5OGT/obJS8/iGuosXpFbies5cx3Gkw4pJ1nMExlTmJSEs3IKLjWTyzsJ2fZ8ujDDq7elpCSSt7bv4R54Y6WK/uRhV7QafvgybWjjzIySByRnE9J3p8erCQdwiQRyelk/9eksQvg3p+SVw3t6fSbfuejIYCICzqTiGiSEc0LkvMZyftTkjcz4tNJbyuwFyeyQHe9of75gvZi5RQqB8wYxI28cJxX1wY3V2TpEDW3zmQFjESkZExkxtyc09Gy8V5f36J2KH02LyH1KoxTOeHMvGcu5ySS9QbPLuvjNv597/0L9IrbpaFmZa9ITc6Ekzu/TjAkkpMzo5XG5z/3VyddMOFUEYMq5RBxAiWfyKx77pUpMemdGWkjERkFhojOuMcUW6dh19L0MzeW9uisdDBON/7MJpJ6FcoTzsw7Tsz5wTk4VSfb4CTEL1h4GfHn2sSMPARbo+ndv23ff/n5WBIiYmJSjlPyFddKrAmp5GRa9NdtmCna/mv3419+/qXQasNGV6RSkOxRWgzn1BAxkRkY9fNgta/k1V4o5ds2jaENMDyn+gA/8xBB4GWtS670oxcPi+6cDe6P1XdhRDhBlNgkROrufzUbanX3eqtdf25Che6u62S7joSzEBwwTf9359UX9dW3VLIbQVwuU3KZUGtFJRs2unze5GtkkDQm/fGMyb/+CZN9myiaRIbk/enerzFZQvJ2DgrRNMNuhtLqegs7FTgXxOXEZzOSt3MXvEWCmMPPn60aPw+3ovk4zsSNjDw6itJSgzrj0ZaaiORgT75gyGTCG/MHUnIu7e8suPBSydW9jyO0j+Qy8S1rJ0zNKROZe0n97IsMrWtC7PoN90un0YoV1xQ6x2p3557PVeJSVCZ0psFoRCz7F6VGa1ahEqTDrQS1NFi1LPWSzBacGMtMTjEHbAQiIqZySmIypnLCiS5Z6TUrvabyapChbfUQhsglEYKFgJkxlVMmckIqeb9h2keofG50yYX9lSv7kVrv/7oYGQbbzazZee/Np298zHgLFkOwRInFqQmaI4I4V9UILeQnYLZ+aupbosNGfLsht1ifNNh+/Hl8PL+Gxs/pZlq44OyAZL5rJy+8xQ0kkrt5Uy5otKSm5usetxeOIibyFjqGyP/Mp3ntlrrik/0FDKSaYyR2LaYH1jdnM5MTSUyqOafmLRu7cq3zbKi17N862j6gc/dD3fk5O9c2xqlQe2OHWGIiXKtkUEhO8UrJktxopwznsKVmCD52obUx+4e3zP/DPxNNv9GWQYT4UCVukpH+eEZ8NiWr3kE31HVX+mpcCOZMEiFpjMkSJJJty+UBusWG+rcrmo8LunWJbdrRYmBk5LFxA8c1la5Z65JcJnuFNbYKgSlTOSUy7mVgbEQqy9641GJBb+b8Ps9fGzF9ABdLSiFTpnLKzAdxmReOCFk89T/P0nmp9g2FTMll+sXxvTRaXEbaVctqUu3cgnqLQmMkCalCIZ2fE9kf5DRUfTvfkGbhPmebkV4QWxcsJcZVMZw34f6KXLBgKHRKqjmJzShl1QevXV+R29bldqsnrl0rIZOiTyhMZM7EnNy4xu4iVIidifmKpV6ysBesdTHo8z7i7meuLXnrv7h7XQimn8W8Ebh9Hsjt/NttZmMKmR5ViXCBn2vdtWJJJPNBmkXFfhHAheDOymcf90HdbsXOfbal084lSwZyPbZas9IFuU5p9c2d6rwBEfcajTQCA4k6u5VIIyrdePXXEODCzYBuu/Zs/3XjGe4Dl/B8L+0ljdZPMl/WUNPZloSUAufFlcuMROW4c0JMKjmg5KzIdeqSWLLpz03nn3uL9Qm97Z3w80TEzRm3hJi0D+JSKdx7MkR2q5c76DDWYjGunTI+m5L9w1vik/0B2ENg0hiTvv4QQVXdJWQtzccFmz//QvPbFbZsYODzcDAGcSOvBEXZ6IoL+xtn5r0PnvYjvqEilwlifmAiJ76Fo+yHqzs6rHb9xrlvyfAbnZiUVFJiyUhIiSW98T4ssrtH2tFSU3Jhf+VSf+ed+ROp5P3m66Xip9Zc5lc3LttJ5gPqXfz2UCBnSiIZCfslfxute1PZoWze9lHphis+IFaIJWWOkst079wfhJmZxF9fMUU0p6Oh1bZ/33tT7bTjRr6a61QxfcY5bFr8JuYYQophows+2V+4sh8p2fS/b2S4hNbtTApiUiIfkG03+qb/27ZlUhC55WP9v9z3J5IdHcQF5cXEZMxoAeVm86Te/JgqeuPj7Hxe+yCwpWajS9a6YGmvKFk9+Dn8Gpx35YINM2pTklH4IOpQd4X4VuuIyMRMOXOiRlrT0vqKkwUfzN18VrZBt6u4fVZNRfpOD1VlpVdP8voNdda1Lvjd/o2GmrfmDxjvRXn4nARcZc5IRK6TvvrWabBzsDeuqu133WwVlluSFO6cxf09M5yvke8X7Sxat1R//8Ty//g3qr9/gnb4+wwYg7iRV4KTOV5xZT+QkjGNTog1cTfpPdk/8TfyRDKsug1zo3XfwtHR0mnbLxhuMXC3fyOuba1vySDpW2S+OL7PKnBrveZSP/Cx+5lCZpzzA7E6D7uXup5oCFDVCc2kWvis8M1zEvziwiIbzuttOFVKS4NTpax18yKCuNbPuEQkJDZzqln+Me6ryImEdqjYVzF2FLO0ofXzIaEitxvEhQy8m/cwBwPGXcL16YLwioW95ML+zkqvaLQaA7gXQC4TzswPTGRGJjlO/y9Uwm+pMjwC2+SWqwZ/C30Qpy4YqbRkoZ8QK1SyodRhBHGdD7g2umRtFyQmI9+pXN7pG+erT/jKEOB9Np3/XLtjXUD/k+SzYMTPvn72mldVWr+WXclHnnJRUZxvZqduxjfF3f8ymZBo2lfKbmP3XPXJpzsO/bYA7rvhZhvGyFfQq1FapVtuaC9XVH/9wObPv9AtN4O3FgiMQdzIK0GpfBtKoVMKO/MDypOjs38SmoHEbYozCreIivW/QW8EH4IbkN7N6N19dEpHQ6WhAvdb7wsWFBcPtYG+FFrc/Fqiua/G3d0qaZD+rN5GR+OGy3XTm2G/pICi0hUX9le3ITMdM84oZMahGbnbMD6XrRKEJvSWa/KOtqADuPRCx9JecqUfuLYfWevCt2G9jMXse0cwbu5HEh/ARX1N4qXirmOfOpOYSMPjGt5jqtjwSX9FrfLGuOfivufeBWFCIu7V/uW97madaXctGtI5UZx761qv+d3+lYo1Z/IDUzkhlvTozoARj6p/e+4DeWVYxdYt5f/3G6v/+29s/vwr3bJE6+7FnOsxiBt5NYTqx1KvyNT1jMeSECt7s38B8cEbXi3xIdbEoPIXpNpXes2VfuBj90vfLtP4ylVQyRrOUvx1BBGSTCfM9AwVdzf8fJOx27q172cFXzin3vayAoqailorrLVug2bClhRfJT6Qrd/5uMjDCuBo38ZmaXwb7EI/8dH+zFoX/WzOyMvASJhhC621L9vKpA9X/H070m3L8JAClkClJVf2A8YYCp2SaOYq4/41e4z6sEj46ghecKATPPQ2unKjCdpgIve85bjZ3DB/CV9XRXvMa+Bm8Pxl2+ZTo6poZ+nWFe2nFdpZZztgjFOuNIJEBowT8XjpStePifqAWDuL3dS0V2s2f/6V5f/+X6l/u8JuqhcTwMEYxI28QjZ2wUfVXrK9kBlZX+V6Wqyrv7HRJVf2I9f6sa/AhQqHm/VYkUqOMn/yY3xonMDJ0nuKNX216GtoqFjplRc0ebkBRaUbLu3vrlXXbJjJGVNzSk6xt4L72LTU1Fqy0AsW9hNLvWKtSxqte1mFkZGRw1g6p9Brr/jIzzQ0nMg5ucz6acTvDfU1uY0u+dD9zMasmMs5U3Pq1mWKQQbkEI692xmpeL71R+uWTkvW//mvaNUSv5kRzwuik4L4pCCaT4hPCkyRYpIYvtFH7lVjFVs1tIsNm//3FzZ//pXNn3+h/vmSbv2yAjgYg7iRV8iGFaWuwYpvacH5s2l6c/D5EbJVoc861DjCMP7CXvDJ/sKl/Y3uxpyDqzaVumSic5AXdge5hY6GUjtv19Bgtds7m3gbIfNZa8XSe5S95La+horGt4XWlLRS+8pJ8HPz1QW5qX/6kNy8Nq2fI3XzmRf2Nz7an3tfphe3ko2MPDMuXLGsdYFapdOOKAoiQzFGY/8ad18/1ODlYXF3mgqnxLzuFtSmdH6Thm3V2LeR3uw8ePzz01fYdNenMOikdv6+XXtFzGcM4poObTrW/+Vnyr98IHk7J31/SvLDiX9/Svr+1JlaTzIkjZ3cfpDd9+/79eU7qNT1M2/ewFutgrXYpu2tBFb/539j8Z/+K+3liu5q/azH+7WMQdzIq8SpVS75YH+mlDUbXTIxcwqZ9Z5tj7JR9tm7RkvWumKj1yz1ipVee5l2+0VrRgj02lciIBEWwUZrNnZJZgrvM3X8HFgIghtK1nbZzzu+dJwAgrsO6q5kIicUMvPV4oKU/BE3d0HsoGKjKza6Yq0LNrrYqb7tSnaPjIzcl86LVwmCdpa1ufY+jTOSAzPCrxt3/1npFZ111bkimGszJZWCTHKeXi1SaWl9V0JFrc7OoGLDRles7JUTaqF9wmO640g766pIF0u0bmkullR//Ug0yTDTnGiaEc1yollBPMuJ5gXR3P99mmOmmW/BfP1BHN42wNYt3aqkW2xoLpY0Hxc0v11R/3ZF9bdPtJcrZyfwQhmDuFfOVqJ5v6LfSzNYPYYNSzZ2yUaWrGXJGe9c77g4aWd067ZzK7d9+M5T485ckNnf6IpL/Y1r+5GFvdwrh+3mvlbUpnLy8Xp7e10wy30Jz85uFTILKpV6fItHMHKttHTzWWxeyCPfjwviWkpds+SCXKacyjtOzTsw6mdogkAJ7G3BCp/ae1q2TlOKpdaSUldc2t+5tB/Y4CTbh0o/tecVCu9i66v1eu5f92VbQdh6rb0mdteoIT8yS+ckmPw871IveWP+AMYyAS9PJOw6j97JUa/xgN74f5DiH9LroaNhpY3z0fSqnCfylhPzljln3tfNVedU9X7r89EPc3v9bGeCKypds7ILVnrpEq/2mpqNbysfyDnsLNpZ2rqlvaVyJFlMPC+Iz2ekP565tx9OSX84JXl/ShIJksQYYry3yJ1Vud4/jaELquiNd/1HrUXbjm5Z0nxcUP9ySflvv1H+2++Uf/lA/evlwB/XcYxB3CvFqR6WXNnfsbS9hPFdWO3Y6NKZNevmiY7yaWi0Zs0Ca92gdc6ETArnTyZpbwAaye4G2tyYY9gYP3bbAAAgAElEQVS6F4VtUud9a1pvS9DsZPJKKtb+XK5pqfceX/CN+2R/odGa6A6bgrUu2ehiZ7M6bNwc2G/ObsDmxHL8oH54jNf2Uz9X95oIFdtaSxZ8orZOVCSTgoScdOfaDJYDYXOza+S8/Wnab96Dt2FHS0tDqzWNF1ipvYdfqe76PHRtPicdLVf2Ax0NMQmx3F3BCGdgaa+otXrCoxwOS3vJz/y5v2ZeW8tUpy2VbihZU+nwW5+C5UqYhy11TSYFmUzc65usl9F3r+/4htLkNpGzk8zwQZkNQZpPbrg/jV+PWlpt+9f8tX4cXCu6Ozeda5fnksZWLLjo1+W0PzepU1wl2bn/3fTDu/lTb/4JZvLBBiKs2623a3GefDWNfx/ukQ0VLcEg/eWsPdpaunWNqqvUtZ+WVH/5QDTNiWaZq9CdTojPpuT/9J7sH9/d+bPsqqK9XNFer2mvN2g9zGpVCDbVWugU7TpXsaxbbFljNzXdquptBNrLFd2yfElP615EB5CtE3kFg0CDRQ5n9HuGnuP8NsLSmJL3LWyFmZHLlIyCVPI+mIu8CtrnG+XtIhmCttJtLDQEbS4Qbqi5//k8JBP9Ep+fG1MO9/7ul7aIfi1uyxYRETORuW/9nZLLjDQEdaS9OfDW/8stYpawUXEJhaZPJjg/Ldc2uew3K7s5+6GzvYLGe9gh7neuXhr6xf9fDi7kiElIJGMiJ0zkhFxcq3kqOSm5TyRu58TCazxUot2b9Ukal0QMQUjtvU3d6969r9nQ+YTOkAlJKYMhlym5TN39zydcM8mJJeuDOuP9MM1uoKufV6Jtv1Y3VP1cckvt2iX9ul3qese65hXdP8T/T3CKlUYwaUz60znZn95y+h//Haf/8d/dmeypf7ui/LffqP7ykepvH2kXA03uqzr1zrZD684Fb3WDXVd0yxJbt2Ctm4nzXz/0p1hVj76Bv6BKnJAmM7J0RprMydIZq80Hlqtf6GzD4J+VZ0Nf6KL37RTZOZPiLXGUY0xMWV2yXP1K09X/P3vv1RxJrqVbLsC1h6RMVXXq1NHnWs+1nn6c//8wZmPWNn3viJ7uPqpUSpIhXQOYB8A9SCaZmWQqMhOripVVxQgEXEQEPuy9vw1ma3dJdeG+GOLBDto2UZW8Hu1g+HJIsz3y/JAonBMKQSpaRqZxdsoVRX3KavszVb28wYy/oC+QgfNH9KUd24ejj8z1ZiNKKypRELEkFNFwbwauLcFr96bZFeMPO/OmpXWRuIZqqHm7b/fY+eQnz5vx5+quYqNnHR1233xlM0T6SJyICOkjcXJ4j1/YQjR9tH2XKrkTdN0QebfCrkW5aNNdi8JdRf/tqjA0prIpjqamFBtCExGKaNjkEuItkbihdYoZRG+/+arprN+ka6bemWZo9fPFvWcupEIajAJtDO3pBhB0ywKj9LVtCXTboTYVzema+ukZ3eL6kpDPiQ1EGReFs+mmplPotsPUHUbdn55vt+HeiDiBIE2mjEcPmY6eMBk/5sXJ/0NZnaJNx12IKHruFlm6z+HeH0mTGWGQslj9g6pZUaoSjaI2JcKcj1S+S9TI3mdH8Z9J935Nlh4QRzlCSvcFolC6Y7n+gaYrbijiPF8zfW+l3snttvfm5cqYL26H2eO5p+zS+RoEGzDn3JIvvK/fXpB9cXPWnHvHX/zzfmFsKqNpAHHpM/AmkebLW4dXnZ37eo5uj+k03aJA1x3dqsB0ChEGVxqdmFbRbSq60zX1szO6k7tcO33+P+5PxO1DcG9EnI0KS6SICIKYMEgIZDg0i/R4LiNlQBDEBEHionERfU+uix/iN2dbn3Cy+gtJ9ZIwTEniMUk8JQwzAhkRBMm9b7b7tRGRkJHbWhUiWho2LGmoPuk8dimkdrc5JSchIyS6YMfdV2jWlBSsaanf6OApEETYepOYlJiEDSu2rO7Ubr1AXphjQMiWFVvWfBXfyp4vnrtvz/J5ed/vZ88b0BrTdrZWbF1CniDz5PXHGQNG76Jb6u58R3h23B8Rd56hTKbve/El5v977jLb8iV1uyYMYqSMGefHzKa/YpQdkSbzzz09zy1ISJlzxFjMSMkp2KBM+8lFXI9NHoqYsMecw6EFQeCEXF8Ht+AVL1z9x5sWh70gnDBnLOZMmPPU/IOSLWr3ofrZCQjIGDNmxkTMSch4Zn6gYPPV7Zx7PB7PB8eArhq6ZYGQEpnFQw85z/3i3og4YwxNt6UoX2GMom5XbLZP0coaSHg8nxKlW3Tb0XUBQgSEQULeblHxDGP8jtV9RCKt7bWLAnW0CN69LcKHRCBIGTFmyoQ9RmKKcalGrft9L9g657r2LvvWkoBAhC4iZx1D+5Slu/Mpag0OQhEN8zxvy36beUoniAWSzhrAf+hJezwezz3BoKuWbrElyGIwo889Ic8tuTciDgx1s6JtSzbFcwIZ0qmaTtV+0ez5DNgCamUMQig63aB05/rx3Z3lsOd+IpCMmHDIIxJh0yk3LFhxhqJFo53RdkhpNjRUqFs26t7VnHyp961w0b2RS81cexHn8Xi+XgzosqFbbAnno10dmefecY9EHGht3YXe0PPV43kLH/rDyrlhGWU3E4zxH4ie90YgSEgZixkaTUNNwYa1OaOjxaBdvCqgpaHj7Q69fSSvMBsQ0NFQmPWdc2Yz/fGaNQhDyYaK7a1icAJBQEhCxogZkYhoTU3N3e8z5vF4PB8DY4xLp9yiK9tyxidT3k/ulYjzeG7HzqHP47kPCAQhMQm5M/VYsjELNiwvRJEEYmgt8DYMmoqClpqtWSIJnRnK3Wo7oFCU2Oji2iyQSBrqW5mvSAJnWDNmKvYIidjgHWM9Hs/XjU2nLNBV6zee7zF3VsQJEZDEE+JohJQhUrw+1apeUtZnGHP1AkYgkTIkDBPiaIwUAU1XoHVLFI6IwgxjbJ+Vpt3QtBvCICMKM+tqKCO06VCqpe0K2rZAG7uACmRMHI0IgsT2bNHa9avThEFGGKa214uQGKMxRtN1FV1X2tS7t9TyCSGRMiIKM8IgJQgiew76Br9aoY2i60rarrQ1Wrp963mVInBujQlBkBAEEUIESCF3ZsVuvsqlq3ZdTaeqa+crRUgQ9mPa8yaF7Wd1cUxFpxo7ble5MS9esSCI3PHGBDJGOgfSwYXUGHfNOjdGjVK1O/fX4PqkCBkSRTmZ0YOLpBAShMAYhdYdbVfSdTVKN+90Pj8sAiEkYZjae1DG9vpg54i7V+39WA7Hfd3977mfiD7GJqy1iXWgrGioaWneSbBdR99PCuoPN+EPjjk3z/dDOqfL3uGz77fn8Xg8Xy19JG6xRZeN39++x9xZERcEMdPxN8ynvyKJrJi7zPOT/5tnL/+NTl29qJEyIIpGjLJD5tPviKMRy/VP1M2a2eRbxqMHaN3SdTVnq79xtvwbk9EDJqNHpPGMJJnStiVVs2S9ecpy8yNNuwEginLm01+TpXO0VnSqpm5WaN0xGT1klB05kRSjlBVtm+I5m+I5ZXVGqd8gPp2AS+IJk9EjRtnxIGitmBFWDKqK9fYZ6+IZdb2iadZv3a0OgoQ8OyBPD8nSPddDzc4TbJhd6RatGorqlKI6ZVu8ZFu+uHa+YZCQp4fk2QFZum+t9p1APD+m6mqK6pSyOmVbvmBT1Ow+PayAiaMJo+yQNN0jjWfE0YgwSKyYQ1gxrGvaduvm9YqyOkU11++u2/5tmkDGZMkeWXrAJH9AmsyQQYxAoFRD027YbJ+zKV9Q1Qvq5tOKOCkDpIwYZUf2HkzmJPHEilkhrchUFVW9YF08oyhPqJsVXVd+0nl+6Xzu77M+TbJvZmtcpM3csbTH+4AkICYjEVbAeTwez1ePAVW1Qzqlj8TdX+7st5pwi3opQqTru3U+ehTImPX2GVIG19bICSEJgojYiaEssYIrCGLSZE4aTxEiRAjQukWphjw7JE8PSOIpcTwmjiYk8RRjNGV9hlI1WndWEKRzxvlDAJRqqJolnaoHIWN7lMVDR3kpw0GQKF3TdiVaXzYjEIRBSprM7WJ+/Ig8PSQMEwIZI1xLhSjM0LpzxxizCZ5hTEfn5neZPqqXJXtMxo8ZZYck8ZQozK14EMGuJbBWLjKlUKqhDpZuMXmRQMZ2zHTfjXlAHE2JogwpXh9Th52LbNZUtT2WXZN2gRCCKEwHkRnHYxuVkxFCyqHHW4JBJTOCICUIUwyGpit2dWlX3ksBcZQzyo8RQpKley7KGyNEABiSeOLuNSvs+nP58SNd1ncviSak6R6T/CGT8eNBwAoRDOcq0iPX887eS+utpDT6E83zfiIGC5CIkNhJo12/QI1C0RES8+6VAVZihUQERIPtf/98M4zc0bmo0lW1Z2KYVz9G31ggISEDcP+VM8JuKvS94Po0yo7GRegu3vsSOfS82/WXu0hF+U71YTYtMSZg957u0zDt3O0mizh3/JrOndkORXttyqYVWv08d+/zHpsGWr5Tqwd79uIh4paSMxFzciZExBgMIzGhMQevPbejdbNtXY3h69g7JxiO+fJ5Ne6q9GOpQXx7PB7PXeFcJK5qz7XtwhfH3TPurIhTqmG1+ZmmWbv0spxRfsQoOyZP98jS/XceSyBcWmVGms7RpqNpVpy2myFqNh1/QxyPrXmK7ijqU4rqjDSZkaf7jLojNukzOlXRtNthbDtuihQBcTwe0i4X6x/ca0vSZEqSzEjiKVm6j5QhXVdSVGc07ebc4lsgZUCazDiY/47p+DFhmCMQVNWCul078wyI4xFxNCZL9xjlDwjDFK06ynpB0z/uHHE0Yjb+hsn4MeP8AXE0ou1KqmZB2xbO5dO+i0PXILtqVrSqQpvuygVYHI/tmKNHjEcPicLURi6rM5uWeGHMhEBG1M3aiiPTXVrPWoMQKWOSeEYQJLRtQV0vXWqjwgrc2AqweMI4PyZN5mjdUtWLa1I0GQSPSOZIGdK0G6p6wbZ4Cdiob5LMiMN8GNNgaLuSpt3Qdh/XBMFuVgSMRw853PsDSTwhDHPatmBV/mI3DowmDGLiaEwYZswm35Kl+wgkRiuadk3rI3JXIFzbgJQJM8ZiTkRMSDwsuCu2lGYziLF3s+q34mvMjDFzEpESkzoRIpx0aylYszUrKrbUVK+lQoaETJgzYkYqMhJnpx8QkWGzDzJGtl+cmDtTEzu/lprKlGw4Y8HJayInJGbOIRP2iEVKRPzacTw3P/KcH98qNGIS26uO3MmzjpoSjWIsZuRMBiHai8uKgsoUbFm9Vst3ceyUPY4YMyMWKSHRhd8rWp6bH3nJL2++KEBMxpQ9MsYk7nwm5ININBiO+YaZOLz0TGNrD82aDUs2LF67D/oIaUbOmNnwGn3rAjB02L6CG2ObqDdUn63PoMfj8VyJcTVxy8JH4u45d1bEadNRVqfU9RIpQ4IgQekGKUKiMCW9yU0nXFRPBq62zlDWZzRtYSM/6SGJE1rr7c+sNr+4dMUaIb5jOn5MEtuIXFWv6Lrdl7IUAVGYE8gIYzRtV1DVS7bFC1sHhrGRJVUxGT0izw5o2g1ldYbWHZ0qUS4dVMqQOMzJ0n2m4yeMRw9p2y1lvWRTvGBbvnTix5Cl++QuAjbOj2nbLU29HuawE3HCCsxozGT8mOn4CUk8wRhN027YFi+ompUTk/acxuGIKMpdfZg9D5ejhdLVLE7HT5iMH5FEE5Ruqdu1bYRdr2i6LcYYBIIoyonCjNbV8HWqubRI6l0eO5vayJqm2dB0W9q2GGreojBD6ZYxglF+RJrusymeuWMyV4q4PlqJEMguQKmGbfnKpb8qmw6aHTLKj5iMHpMmc6rqjLpeYtAfXcSFQUwU5ozzB8yn37lrU1DWZ6w3v9C0W7TphqjnODsmTR4TR2PbdqMr0abzIu4KJHLotzYVe0zZd9GTYIjBhUSEwgqHXoiIN2xH9hGelJwp+0zEHhExEckQjeqjexERgQiRxorDhvpCRK6PxCWkw5hWxAWDmLFpgMLVx6nhXdO4j++K7ZVRNuEEbC9kdgJxF0HaiAXCvB5lv0xIRM6EkZgBtrauokDRMWJCxngYu+9fl5CRihHCCFpqGuqhIfnlaxQ5+5GUjGiYp/2ro2PF2VtmuLs2EQmpyIY6uD4a2QstWxsXXXqm7bfXUBMQcFXLhWiIiE6Zsk8uxi46GQ6ZCtrJuEDYc7ExiyEK6yNyHo/nrmCaFqU1qmzRrULEGqR443ef5+5xZ0WcxaCNwmgb61WqsfVat0wb07qjqs9Yb55SVmd0qqJI98nSfeJo5BbFaxarv9N1FVorkmiMUjUIQehSOgeTDbDCQIYYo1lvn3K2+gdVfUbdrIc0yr7WyhhNGs8Iw4zp+DFK1y5FswEgDFLy/Ihx/sCKEt2xXP/EcvPjuUicHbNu1myKFyAEaTInDFNmk29RumFbvRpMOaQMicJsSM+MoxF1vaKoXrFc/8imeDGc155KLpAyGqKS6lxEDSCQkY1qJnvk2RFhkFHWC4ryJYv1jxTlq9fGlHWElAFaKzfmVUYpmrI649XZvyNEYFNcdetSBfXw2m1XUjdrpIyYjUfD8dm6xNevuxXwIWV1xsnyL2y2z6jqJZ2qMEYjRUBZL6hqe9zT8ROSZMZ0/A2tqijKV7e6396VJJ4yzh+QpfuEQcp6+5TTxV/Ylq+o6gVKN3aeMqSsTmmaDUGYkKcHZMk+ety6jYHTjzrP+0afAjhjn0PxaFhsl2ypKIaFdeikjhViu1S8q8eURC7CNRdHw/NqSjZY0W8wg6iLSTkgJxEpwjBEZ5SLyCk6CjZoNFuzIsKaAvVRtBkHVJTOZt9GtnoxoJxgKNlemf7X0bLkhIqSyMTW61JMGDEhZURKfuNzGiAJiW2GAblLPWxYcTbU7IXDscdM2cMIjTKKLUu2rDGXopENFWe8omBLZCJiMkZiQs6ElPxGRiQdNVuWtKYmJCIlZyxs1CwkQqNZ8oqNeb2Gtk8trSgvCE3hkigzxhyIh4yYEpFgMGxZ02E3pARiuB9GTG10UoTWOMsZ03g8Hs9dwCgDRqHrBl1UiFAi02gwz/PcD+64iAMboVEobQZRYW4Z+tVaUTcrF4VZolRL3SxpmhWBjCBiEFx9RKduHqNUY/sN9W6B50ScrVATKNOxLV+xWP2DTlVW+DnqZgVAnh3STLZIGTLKjynrswuum6FbmOfpAWGQorQ1Qzlb/p1OVRfcEmtWgGSUHTIbf0sgY8ajhxTVCVLsFj2BjIijMWkyI01mBDKmqhdOHP58K4EiAzdmPCNL5gghKaszluufWG1+fi8x0buEXocQkqbdolTNbPLNIK7jaEwgr9+tFwjabstq8xPrzVOX6nluMVmf0XUVY5deG0c549ED1sXTWx/LuyGIownj0SPSeIoUod1IWP9AUZ04AX1xB98YnPnOlDSZArDc/PSR53m/sFGokJSMiZgx59CKIGx638qcDjVkuRgzZkbgarMCYaNJl/cYbM8xGzGaiD32OaZ19WglW9bmFIVCo0jJyZkwFjPGTBAIGlGhTF8jZ+89jaKmoKMZavekseIzERkzDmxLAFaszYItyyElU5+ru7sqVVHRsWVFwcbVncXMzCFC2OO4jYgT5+rsFC01gpI1a7Mc6sBsBCxnLg6Y9EJPtCjTUrJ9LaW0o2XDgi0rAgISMpRprVOrk9Xvik1htYLXCq8RwsjhM1HRsTJnnPLsivPV/9Vx/uL3NXA5E+YckZLR0lKyYcOS0myG5us5Y3ImTMSclBEtNbUoMQYv4jwez93BGIwy6KKmPds4B2wD0q5vdd1iWoVR2qdb3mHugYj7cBijUbqzNVZuYaz7/6dqJ5S6C2kvpo8GGm3TMs+ZF+x+b1MAO1XRqd6s5HW6rqKsF6TxlCjKCYMUzgnCwDlSRlHu2h5sXephdY1phaFTNVWzIkvmJPHY1ufJXTpQP2YSjZ2hSktRn7EtTy6khd6EUMakydQZgwS0XUVZnVJUtx/zXTFGDyY0WndgNLhWEufF6+XnKN24dgnVENl6/XH2nNfNiijMbQsJeTnt6sMThSlZMkfKiFYVtq6yK9GqvXLDQpvOppl2JWk8JY7yoV1CH6n92ukX8BP2nEGIYMuKE/NsiMT10ZbGVBRsmIsDDnl0rYuhrYeyY6bkCCRbVpyZl7auju0QiaupKFijTEckYiQBY+Yo0VGZktYt6A1mSLcTQ8zHvn4vzHqjjI6GhmoQn2b4S19bx2dckp/G0CHe22yjb39g0KxZsOKMrVlRsB7m0VARsCJ05wsgJR9qBt80TwWDyL1N/7r+XPUpQVZs9umMxv2+eU1Q2XeNHs7neSISRkwZiSkxCR0tZ7xkbc6o2LoedjYSV1FQsHFRuZiYlDlHLuK6uvHxeDwez8ekXWwp//6CblUSTnNEYD+jm2cL2rMtumiskPPcSb4qEQfGpfI1ThS5nmO6sxb4gzA490Xr+pL1X9JWyJ3/tbEujtr2P+tTI6+iUzVNsyKOMus2GcQXonpShsTRiCjM3VxbhLBmHlcjkEKiVO2cHbNzboYSY5SLmo2IohFShLTKmoWU9emFaOFN6CNxcZQjRIByxiJlvXjj8b8bvStp3xtOuui+GH6PYNdyAIMQ4kIPvctYEdcOrR6ucu8E0EZZM5NmQxTmRE4cfUwEgjBMbbsDGTrhppEyIAyTK58TBom77zrrOupq/vpr7rFpj4lLpYtIAENhNpzwbIgY9diojUAayUwcEJv0mjGtKBmLOTEpAijMhlc8RV3jaBgSMcFGZUZMaGkIObnwGCvKdl+S0gm6nVjrXSjbK10o30Yv9fo4k36D6Hs7VsQplBOwL6gorjTvGJkJU2FrEBMyIhEjzNUibjdPNQhNdQsR14vino52aM/Qi7TOpaG+KxExY6bkLiVzS8XKnLDg1TD+eSoKG4VlTkTCjJiNWIJ5vc7O4/F4Pifd2Ybqr8/pzraEs3Mi7tWa9tWKblthOr+uuKt8VSLOuH9eTpUy4Ew1tP3z2hEEr/uvGsw7pnnatFAbXZEicNGjPrJn6CNKtg1BhMxDHh7+d+aT766dTpbMSZO9oYecwAqavsl478wpZW9TbyOPvVi4DX0TdeHGxIkkrdr3Lt6PwsyZyEwGJ8ZARq7pdzCcrzBMmYwf0/fSehPGaNc24W2LV3ctXUN3Kfpj/LgRrr4vYH/d96bfE0cjlLra5tyKPtsiIwwz2m7rGqIHCGG8kGNn6tELuIaKjsYtui/fo/ausGb9lU2pvOKjsbfsT52As8236ysX8j2KjppqaBkQE7sar/u6oD8XzzKVMze5+j7to2KRM2+5r022A0ISkQ9RvdY1XbdOoVdE9DF0pqEWpTNq6R0s324g4/F4PJ+S9mRNYX5GpjEy2dXEqbJBrQrUpsI0V298ez4/X5WIAyvWMOa1xbzBoPUtcn+NQRuNOWe+8abX1lqBMQisOEDIoQeYEAIhQmQQEwYJiRyTJXvvXAOotYsYDlGsXiAELtVQDBEcbd4+3+sQQlwacxfNvP3CtG/2PWKcHzPKjsjSA2e3n9mG1y5lsj8+KYMhzfVN2JRXPaTQXvs406drKnecwdBc/WMuuHvjlSBICMOEKLSOn+/6kn37BSkCjFA+fR3A1cTZ3mAMC+/rbO5xEZzG1LSiea1XmR1REIuERGS2jYBpnHgzLk3w6ihTZ1q00K4fXDK4X97X1Drt4mUN9Rvt8xWK1rSELp1UvsX1864SEpKQEorIRkSNFe3CVUlepm+z0NK4NNKEkNBtOL1PFNTj8Xg+LN3Zlu5s+/YHeu4kX52I++AIu3gWMrzoWnnVQ6UkkCG4tDftmmoPIs1oZ7FvTTdq1VJUpzTt+p2ns1z/SNeVGCdEeuGoXfqocG6aUoRobifkdmNqN6YTITK6tThM4oltGu4aXYdB7FoBvEAp50jai29jCMOU+fQ78uzorWPvhOxbro+wwk1K+7YwusNo7Y7n4y28ehHcqQptWup6SVGdXpv2eZmmLSjKl+9l+vOlIWBonN0n6b3L4lm7ZMPrRu1bPffOl4fiMamr+7qKhNT2EyNz4m3XfLyvwLpP7Jqjv2uqY99K4f5yvsG3JGDKHkKIa41KpHOyzBgNabf9NTdILrtzejwej8dzG7yIe28EwvWfe9suc582JxCudYK6IBBsNFChdYPWDVW94nT5FzbF83eeTdNsaLtyV/OHtkJR21RRnOCSMjonUG6KcWO6qI8QNv1Thhh1u35ISTxhNv6W+eRbppNvaLuS5foHNsVLyuqEulm7usMWY5SL0KXk2eWmva+zSzF9WyTARRidiNOmG8Tvx6Q3awGNUrDa/MLp8q9X9ry7CtvsfOVMee6XKPh42HrRgMAaW7w1lXaXKHidUYgVhn2b78im2ZFzyKObzYveIOnjbg58DPpzdHNzlPst4+ydFA397Cbs3eD5ZhCC16Xdejwej8dzU7yIe0/6yFYQxIRhRhikQ7riZaLA1jLJIDrnOrlbCNmWByuaZkIc79wk62bj6vXevmhSqrYC0S0OtWqpmzVNu0GbjkBGZMkeeXZge47p6+38r3+Nxs6z22JMRygTsnSPuj6wEaTu5vnTYZCQpXOCMKXrSoryhOX6J7bFc3euatfoHFc3mA7pnG/DRtdszVkUZgRBcmX9npQhUTQiiSfY/n7bD2DU8mYMhrYrKaszkmRKHI0BrMFK21/3t9Vaahu9xTtT7rAptErY5thSSIR5yyaLi5dcV2fZOxhqVw3V0Q5Ol+/KlpXrQ/bxNwc+Hl/bfbZr5WDc1a/Y0l5TC3jV89fmzGY++IbfHo/H4/lAeBH3nghciqQTCFGYOXv4y0JG2AbZ6dz1LCtoL4k4rVuaZk3TbonjiXMr7GgaK8DeLb3u4uLKisA1dbtB644wyMjSOXl9SNsVb+zJdh2dbnbCUCuCICZP9qnTlbO+v3l+deAEbhgktF1JUb5itfmZbfninIgxVpc/K6QAACAASURBVMDJ1LVSCN9pf18ISRBEhGFKGOb2NYYo6PAopAiIozFJPLWNw9vNhYblH4uuq6jqBWGYEqQxIOhcQ3P9DrWWlq9pUf12emN9hXLRs3epxxKudutiG5Hzo2o3av+z4BUL3r3XYmtq6qHRuL9m94FdCmmLAgo2nPGC8p3Fu6Fk60Sgv+Yej8fj+TDcaRFn3RblhWhXEERDbZONyGSofqFr+vTBT7dTbDDOEj5kMnqE1h1FdUJVLwY3xDi0Fv+T0SPiMKesF6y3zygv1T11qmZbviQIEhLnPDgdP0EIQVUvXTTN1tH1pht9A/LeXbNpN9TNenAoVLqlaTe2BUB1RpZJ0mSPvRlIGRCHI9ucXLfW2UOAFNFg4Y+AutnQNOthF7mP7lXVgrI+JYlnZNk+RvRtEsa23cIw5q4ObzfmmrpZD9fJGEWnaqIwJwgS4tg2KO8ji/Z6R0RhRp4dMM6PiaOxOx9vu9YGoxVxNGJ/9muiMKOsTmi6wl47YVs75NkheXaAlCFN+ZL15hea5up6RIG07SacMU0YJLZXmwycWY1092tyoU3F6/V1hrpZsdr8RBik5OkBaTLjYP57iuqEulnZKKS7T/qavVBGCBHY5aWL4Lbdu0eEvnzMYMmfkBERExITEA4pkxcRNmFOxETEV/YzMxha01BTEpG49LiOyhTXpmBeRjsLfb+Y/1Ts+uiJc3/dBOswWhKaiBjb9qM1DRVb+vYNb6OjeefHejwej8fzLtxZESdcZMQuWgMnVmLX1Ng6gkkZEoUZWlvjC5vGqIDb1nrdAmfyEYYp0/FjknjCcv0j6+1TNydl3RbzY8b5MVGUs9k+Y735hbI6uSjiuopN8YJAxkxGj0iSGfPpr8jTfVbbX9iWL10vuhYZRATS9oBLorF1X9Qd6+1T2rZAOeGjdUvTKqp6QVGdEEU5WTonTWZOLI6p6iV1uxmcOaMwJwoz15JNsNo+pW23wzlVukG3HaUbMwwz5yQ5s66a8cSKzm4DxqachmFGFOzGXG5+cumCxo3Z0rYlSTwlDq2Ay7ODQdwBRFFOGs+Zjh8zyuy5fJdIVW8cEkdj9me/JYlnLDc/UdULF51MGOXHjLJjRukhUoRUtRVWdbO6YkSBkNIZ2th6yKBvC+Hq7qQICFx01prAdBitbBXUJSFXOaGWpfso/S1Zske8n7MtX7LaPqVp1sM5sEI2JY7GBEGE1rZB+XKjvYg7x64fWE1KRkxCRERI5PrEXYzCWp/BkJiUkKv7Mmo0DdZWPyQiIEEb4xpwK9e64O2LdL+Q/3TY7bydeLqNO2bnhHosUhKcMykttbvu75Iiac790+PxeDyeD8GdE3FCBERhRhyNyNJ90mQ29CXL032ydN/1RBPk6QFH+3+yaYnOgVHrjrI+Y7N9jrmiLu1DYzC291trIypNaxtFzybfYm3zBXE0ctGphtX6J1bbXyjKV9TNdogygW02jWoo6zMW67/TqYo4GiFlRJbuE4X5UBsnhLAmJSIgkCF1s6brqgvjDXM0mrrZsFz/iNYd4/wBcTQikDGj7JgknqF0M4i4Xjg3bbFLjbzksm/HXLNY/YBSLeP8eEhVHOfHNorWR+LY2fW3XUHbFlxOV2vaDevtz/QOmkJIxvlDknjqGrBrF+EK6FTNpnhOmsyIwtzO/Q10XUXhnED7qFae7JHGU0AQuOblYZC4iFbJpnhGWZ3RXjIXicKcOLIiM01mBNI22U6SKaPs0EZQg4Q4nrA3/Z4kng596pRu3NgvKKtdw2etW1qjWRfPCc/+gzSeEkcjwjCz0d3s0BrJwK69ggitg2mzQuvb9/z7UtFoago2ZkkqMgQzcjHhwDwc6tj6GsKImIiECXMiIvvewry25tYoKrZsTDws6HMx5sA8oKaiphwSLvuIjzXEcK04XHSwoXpDq4MPyy7yZEWqddbc1fz1Ni3KzW8neD6t4Dg/z5DQnbVg+L/WHTK8IMreZZ7GWf13rrl3RETOhAl7F/r79c3V+8TJ8wYkLQ1bVkTEZIyIiG0TcyOpqWhpLkX75HCecb9pqN7YjsHj8Xg8npty50SclCFpPGU8esj+7LdODO0MRKQInXugYDx6QJrM7U6oMU7EtZwt/0bbbqnqxUefrzWVaOiMXfjXzZpx/oDJ6CFBkAzREq07NsUz1ttnrLdPKaqT14xNrBlDR9UsOVn8F0V5ynT8hHH+gDSeEo0euuMPhrYBXVfRdZWre1vb9gJX7Aw37YbF6h9UzZKmWbtzt0eeHiCDCClCcOl+Srco1WDMM5sWqrsr10pNs+Zs+Tfq2o45yh+QJXPy9JAgiBAXxmys+No+o6rOXhuzF4TaKILAitbp6PEFp8i2LamaBavNzzTNhsn4MaPskK578+Ko7Qo61dB1JU1bEEc5o/yYNJ4RBFaE2fEL1tunwzWq6uVr4iiJJ4zzh8wmv2I+/RVhkAxtJoaUUSlJg4ij/T+i3HFaAVlRVmc8e/lvF0Rcb16y3j6lblZMRo+Yjp+QJXtMRg9slE/050GhVEunSup6hVINTbu5tjH414pBU2I3ICbMARgxIxAhaxaszOkQOcsYMWJGLsaERFwnDDSKgi0GyJkwZsaEGanIWLNgbZYoWjq6oQ4vIiYmGURCyYYV3ScRceeNWnqx1gukvg9eP8d+fjt3zk9Xs9fLnn6uoXP+3NUxulRXknM1ieebQVw/z75fW+vSGUMSxmKGNoqKgppqEF625s2m4DYXRFzNmgWRiZmIPRIyDnhALsasWVCa7WB60re1sJHfeKjLXHHqWhL4aJzH4/F4Pgx3TsQZo+m0XZhuy5dvbeR8+bnGKMrqFKUatNF0XU1ZnXK2/CthkFK4FMZ+cV41K9bbX6iqjDDMKKrTCwv3qllyuvwrxmjK6pS6WaF0d+nE2WhcWS/YFi9sZKUrCVzKo1INSrdsy1cU5SuqekGn6mujJ0q11GZt+7AJQacqkmhCFI2GJtt9qmanalRXUVSnNnLUlVeOa4yy6XaV3X3vVE2aLG1anmukbY/E1lgp3VBWp7ZPXVdcKQy16dCdoqzPQEjarqJKFkOqXy88dsKwdvM8penKC4tEpRoao9kWLxFCUtYL4nDk0mfFIFjrZsW2eGGNWoyyrQdUTadq2vaioUrdrFmuf7LpT6oeHheFGUo1VPHSik0ESjc0bcG2fElR2lq0qxxGrWjaUlYnBDJEBtGV1/D1869tLWG7vibt0dB1JVq3CCFtpLNekcQTgiC+IOK0bum6iqbd2PrLZvXWaOTXRh/1qihZmwWJyIZ0yRFTAhG6VDgzRH0ULSUbQmPr4q4aU9FSU7I2ZwQidLV2ESkj22zdiYuL0iQY6qo+RRu/XrDFpK7ZdOpmEZKLMTnj4fhyJhzyaGgZ0Iukki1b1qh3dmC8zTytab+d52ioRQyJyMWEjJHrzSYZixlH5rGTlnoQRyVrtqzcOX8dGz0t2JoVqRgBgggrxlJGLkJnI6+l2VKxdZHUZvh80ihaDAUbFrwkZ+KitzFjpiQiGyKDfUQxwNb/NlSo4XPECziPx+PxfDjunIjTuqOql7RtwbZ4SRBcXZ9yFcalQHWqpu22aK1o2y1r3VBVZwgR0HbWNr7/grZpjStrQS4CG8k6J4K2xQuadgvGCjWlW9fTa7J7YSGG9ML19he25atBfCCEM7Ywrs9Z4xpiv6lfkK2za5sta9WwLV64KI+z1Hdj9lGuQSTp1jaofkNqXacqtuVLynqxixwN8xxe/dLxvmlMQ9tV6OIFZXX6xjH7fmhKdWjTcn5RY4xCKUNRndC0axfR2kUM+mPVuqPTDcbVggWbX4axL7cDsGNt3fh6MH+RUrLePr0wvkFjtI0Y9sd8FXW7plMVRfmSk8V/udS7t2MAjEYb5dJJr3iMsQK6rE5pms1wzYWQu80M1/Dcngsr6JRu33I/fZ1oJ51WnKJMx1TsM2WPnDFjpoMUqCgp2bhWAYaEfIjevT6mjewseEVpCqbMmYg9IhJyxoNs2/VTs1G3gjUtLS31tYLjQyEJSBkxYc6eOGLM/EJEq49yAUzZIxOjITmxd9084RmNqT+qiAsJyRgxEfvscUzO+III6lNRBYIZB+TCfuael3EvzE9UFGiu3sRQdO66CiKTgMD1eevPCUMK5VKc2PfnpbGMk+8Fa5TpyJm48zYmY8xkMMIR52bWusTZgpqS7iOeR4/H4/F8ndw5EWcFTOuiWe9v1GCjRR1dV175e+UiNNfRuejNmxBuJ7ePwMHVr3UzzDD3DzOeG9VoezxvOaabjanolOJ9+9jaKOCbr8d5lG7euDRSqrm6z5sCuHkbBLCbDFrbDmEfB3P9vD03RqFddMW6mGqhCF30rE8brCgozNp1fzPElHSiHdLwLqNRFxbm2mgSkZGQOREXnBNxLZ2xfcVKNjTvJOLsM7dmySvxC2sWFGYzpAS+jV1Xs4aaiuAG93ovQlrTXIi+d7QUrMFAKCKXcvjmNOaGig0LrJ/nhq1Zoc59SFhppOhMQy3KG5mODPN8yzmxr2GjoGsWaKPJRD5cK+GEl3L3QUt9baqrNcTZGdi0piERmXMqtamgvYTrjL13CtZUFF7EeTwej+eDcwdF3H3l5q5nHo/nY2OGxXdLy8YsON/Me9cDrBvElUQONXP1NWLd1mN1bFlSUyBNMIgC4UxC7Pi7FMWODn3uda6jFycnPGdjlnS0dDR079iaoE+HbGnYmjXBDT7m+/Fb6gsCtqHmjJesOUMYK1beJuJs9LF250Y6g5GdmOnohnmuzdIZwNxsnu9qEqPctaooCC5cq52DpT3PLfqa82zc3VJTomjZsByOjXPXvX/c+Uisft8dLo/H4/F4LuFF3HvipZvHc7exoqlPrvxw9M6HV0XrPsTYFbZG6+bPNYN4uE6E3pQ++niT89eLouswTtLax3zc9hgf7lrtzq3H4/F4PJ+T1zvaejwej8fj8Xg8Ho/nzuIjcR6Px+PxeDwej+fLRgqCLEGmkTWMk6/n0+miRm1rPomd9HviRZzH4/F4PB6Px+P5opFJRPLNPvHDPUQUIqPXa7HLvz2n/MszTHv3a5m9iLsl2nQ07ZaqXhKqmrYtvKOgx+PxeDwej8dzB5FxSPxon9GfvkGmETJ+vdevrlqqv7/0Iu5Lpm0LFqt/sC1f2ebbuqOoTj73tDwej8fj8Xg8Hs8lRBQQH03JfvPAirjkdRFX/vUZBPfDttCLuFvSqYpN8WaLbY/H4/F4PB6Px/P5EWFAtDcmeXJwrYgLZjlC3g/fx/sxS4/H4/F4PB6Px+O5LUJAIBFRcKWpyX3DiziPx+PxeDwej8fzZSNsNE5Ewb2Jtr2J+38EHo/H4/F4PB6Px3MdAoSUCCmsgBP3PxLna+I8Ho/H4/F4PB7Pl0vfFy6QiODLiGF9GUfh8Xg8Ho/H4/F4PJcRAhFIZBAgvoAIXI8XcR6Px+PxeDwej+fLRIAIJCKUX4ShSY9Pp/R4PK8jQAQBMokI93KiWU4wSpGjBBmFyDgEKRGBwGgDWqNbhekUumxRZY1aV3SrArWt0VWD6fTHm24UEkxSwklGOEkJxilBFiPT2KZNhBKB/eA2WmOUxrQK3XToqkEVDWpT0q1KVNGg6xbUx5vvXUfE9trLJCbIrA2ziO11l3GIiENbHB5K+2cgbY2BFHaXs/+SNMb9YUAbMAajDUZpUBrTaXvPdArTdOi2Qzcdpu7QVYsqG3fvqI96/3jejIgCe+3zhGCUEKSxuz/C4b4Y7of+PpDiQs2JGa6/BmXsdW+VfR+29nrrukWXjb3uZYOuWkx39xvuejyeO44QiCCwn1NfgKFJjxdxHo/ndYRARgHhJCX79pD0u0PiB3PioynBOCMYp8jIfiAaZRdiqmzQRUO72NKerKl/OaX68YT6+dKKpq75aNOVSUh8MCF5sk/6eI/k4ZzwYEI0HyHTCJFEVsIJgWk7dKNQ2wq1rWhPNzQna5qnZ1Q/ndK8XNlj+lpFnAAZR1YQz0dEeyPCWU44yYZrH4wSK5IzK/RkEl0QdAQCjBtP60HoG2XQbYdp7I+uO1TVoosata3pthVqbcV0tyxoTzd0iy2qqL2I+4zIOCQYZ8RHE6KjGdHeiGg+IphkhOMUOU4IssT2XervgzCAfrHkxFu/eWLaDlU6wbat7LVfFrTLgu50Q3uypj3b0CrtRZzH43lvhAACCWHwRRia9HgR5/HcE4JpRvJwTnQwIRglyPj1JpUXMTTPl9TPl6hVSbcpbTTkDcgkIpznRPsT4gczkgdz4uMp0dGUcJoRTvOhQaZwxcH94iwYJeimI5ikdpF3OCF5ckB7sqZ5uaJ9uaJ+uaRbFHZh9pa5vAkRBsg0IpyPiI9n7mdKfDQlmo8I5zZyGIySnZ0wWBHXaYxSdr51RjjLiQ6nJA/mpN8d2QXkqzXNyzXNqxXdqsC07zffu4ZwAjwYJfY85QlBHttISxYR5Akyd2KtF2xpbK+9u/4yCmwUJjq3uxnISxEYY/92kTijDTiBbDqFafUQfdO1i8RUbnFf7Bb4alujigq1qVHbim5dolaVfXzT7gTjPUfmMdHemGjf/gSj9J2f255taE/tT3e2vZ34ce/pcJpZET/LCaf5Lso9zQim+XC/BJkT8H1Uro/KBsJG6uVOzO+EvP0J+0h43aLrDl3Wu2u+sde4W5VW1K/dfy8L1KbCtN3dFvUCwvmI7NfHxIcT9764+e6/fY90NC9WlD+8RK2rjzDZ+4HMY8JJRnw4IT6eIbPk1mPZ98ma9mxLd7a52/eS58MgBCKU9vvKp1N6PJ5PTTQfMfnv3zH642Pih3PCaf7mJxjD6l//xupf/0b14ysbzdBvXtjJLCJ5ckD++0eM//yE9LsjZOLSpaSNsOzS5YSz7HXRlyhE5oZwmmOUJnWLtW5Z0DxfUP71Oav/8+92kV6BabpbnwsRh4SznOzXR0z++Xvy3xwTznKCcTqIy2ERKbiw8yaiABFJZBxixgnR3nhYWBql6RZb6qcLir88Z/M//4FpOpRp3mu+dwrhIit5YqOrD2c7IXwwIdofI7PYnqcr0iSH6y+w/y7cOab/89KLCWwqq+x/HdiFvTE229K4NLtLKZdW9NmUS7Wt6TYlzbMF9bMzqh9PqH44oVsW6LYb0jbvO+E4Jfv+mNEfH5P/4THJw/k7P7f4z6ds//1ntv/x1Aqdm4o4ATKQyDQieTgn/faA9Lsjsu+OrXibpOdSZ51IG+4FYd9iV90L7k8r6CQiMq9ff22v/5BqqbV931Ut7ema+mcb1S///pL66Zk7vo8X2X8vBCAl8eGE+f/2B8b/9C0yjmxk8ob0kcr1v/3DCtivWMSF45T0m31G/+1bpv/r90QHk1uPtf2PX9j+vz9T/OdT1Lr0Iu5rwBmb2AwBL+I8Hs8nRoQBQZ4MkbJob/TGxxttiA6nxIcT2tPNGz+4glFCdDgl/Waf/HePyL4/Jv32gOhgPPRVefPk7O+vepQ8VzODlETzEdUPr2yaZdPdaLEp04hgnNpF5q+Pyb4/Jv/tA+IHcxsViN/hI61fZMrr5yui0NYBRQHR/pjqxxOaF0t03dqo3H0hsDuPNgXSRtbCcUrgoqrRfEToUuPCuUuZnGTIKLDRgw+ZdiJe+5crz/9ljDYE49SK9Dwh2hsRH05Jvzl8bUd9qGe8r1FTKd09nhHtj4mPpu/81HaxJT7dUD9b3Mg+W0TBEHXrPy/iBzP7czwjOZ4h+/rS9138iOEfXPy31zFKE3ZqiPiFsxHx0YzmxZLm1Yr2lU257JaFTX2+K9e8F6jaoFsF2n6+vnXT7QpsZDqnfr4kOpzQLrYfvb74rhKMM5InB6TfHhAfz4j2xzd6vjm30SP+GqDWJbpsLvx/zxdM3+Tb18R5PJ77QuBSDoMsfuOKKZyNGP35CaM/PiH7zTHJg7lNj/oAvVRkHNrUsCy2i+8n+yz+9/9ElQ3dqryRiAtGCenjPUZ//obxP/+a9MmBTQN0RisfAplExIc2ZTV+MCN5ss/q//gLuulsfdZ9EXFSIF3KZPJ4n+TJHumjfZJHe1a47Y0umpMMqXB3zL3LRQ77FNr4aEr2/QN02w2pusV/PWP7//1C83xp6xmbe3KNPiAyiazQzeJ3v35SIJOQ5NGc7PsH5L9/RPbbB67eMUZGzsRGindT3B8SKazAdOI9fjjH/PER3aq0kdh/vGT77z9TVK01w7lLkXJt0EVD83RBdXhqU79vIeJEFBIEduMrOpwQvVrTnHzc+uK7SuAicfHRFJG8x9LVQLcsKH94RXOy/ioF8deI8JE4j8dzrxC4urHcpscJ8VrpkMztDnf2m+NhAZcc2/q3DzYNJwyES9VCCsbLArSm/NsLqp9O3rqTLpMImcek3x4y+tMT8j88Iv3mgGhvvEvr+9DzDQMCd97UtgIBxX89o2q6wV3xTuGibjKLbbTN1TBF89EQVekjLcHY1jjdB4QQNo03AKKLKWlBlhBOsiE9tP75lPrZgvZkbVMtq/bzTPoz0Is4mSdv3mnunWdzu6kSP5yR/eqI7Lsjkl8dkD45GIT85+yn1KfqCmlTtQMTA9jrHYXD5k04yamfL4ZI+V255qpqqJ+eEe2NSB7MdnWbNzilNm3VRtKTR3t0p1u6bYUuvyIRJ8VQpxkfz4j2xshbpKaCS08tWxe536KL5u5EcD0fF+GafPsWAx6P574gU7ewy6IrHZnCaU7+h0eM//SE/HcPSR7N38Ew5baTsTvr0d6I8T99SzjJMErTvFqh6zfvpAdjuxOf/+Ex03/5nuTJvjV9+JifxVIgQptOOf6nX9n5th3t2QZdNOg7JuJkGBBMMuLjKdl3R6TfHBA/nBMfTp0hSXwutfXLSCeReUwUBQSjlPTbI+pfTin++pzyv56x/c+n6Kb7ahZpQRpaE5L8zWmPQkrr5no4YfLPv2b05yfWEOhgYjctQnmn3dtEFBIdTQnGKfHRjOx3D9n8jx9Y/18/0J6s74yI02VL/fSMcJKR//Hxe40VjBLSJwd0Z1vqZ2d0Z9sPNMu7z2BiNc2IDqeEsxwR3U7EdZuK9uWK7nSDKuovqp7W83b6zdkvqdm3F3EezxeMTCNrBZ5dXNjZvk/WwGD0u4dkv31AfDQlvIEb3k3pd9b7lDiEIPvl1LYjeL6kfbl6/Ulu5yw+mjH6w2Py3z8keXJANHf1gB/xs7iPAA3zxZD98oB2UVD/fErzYvnxXvwGSFcnFu2PbT3TwznpNwckj/bsNZ2Ndn27vjBkGEAYEKQx0R6DCY+MAhBQ/RjTnm5Q2/qLX6yJOLI9EgchxkXXTmnbhgTTnOThnOzXx4z+bDdvwnFGMLq9298noa+7DcTgihmMbI2waRUIKP/2Agx2gV41n9W11HSdNXV6taI729JtquH+vClBnpA8mtO8WtnNKynB6C/GlfVNBFlMdDAm2hsTjpNbnT9gSKOsfjyhPd1YAfeVbPB42BmbRMEHK724C3gR5/F8wcg0tnb7WXxhd12mMdHBhPTbQ/LfPSL99tA+5lMgBCIKCacZ+e8egtas/vVvV4o4GYd2F/pXh0z++XvSb20N3CfFWROHk4z89w8Bw7JTd0bExQcTxv/Lr2wt46M9or2RbRWQxsgkHJwkvwbCaYYIj5CjhHB/TPQfT1n/D+uIapT+ooWcjALEOB2cRZES9G6hLyMbqU2/OWD6L98z+uPjIbJx2/S0z4ozKghGKfnvHxEdTgmmOUZA83RB/WzxWVOejdLoytb9Nq52Mz4Y30qEyCweHGSDcYqMA3TL3Uvp/giEU3vPRocTRPQ+tXCG7mxL+fcXtKdrL+C+MoQQ54xNvpwvRC/iPJ4vGBmHCM4v7AQYQzjNyL47JP31EfHDOdHs5kX3t2UX4YpJHu2BMVS/nCH/+tw1At6ZUoSznOTxHul3R6S/OiTaH9t6nU/4GbyLIMYkj/cw2lD+8Irgr7Yv3ud2q5RpRHQ4If3mwJ6j+ZtdS79I3P0gE9fDLg6H6LNal5hWW/fKbf155/kROV932qfO6rp1NXCScJaTfntA/ofHjP70hOz7Y9vn7z4KOHafI0hBdDAhnOfWbbBTFEHgegvWmLb7PBErg22PUdQ0L5bUT0+RqW2NclNEFBAE6RBxD2c53bJEqy+/Nq7feIgPJ/Y77IapcKZvYdIp2sWW6sdXtIutbWNyV+nbdRjzVURbPwnuc1B6EefxeO4LIpCQ2AbNQRoPi5zoYMLov31D/psHny2NSoR2YWm0sU3Fj2Z0iy3dshgeEz+YMf3n7+088+STC7jL8+1rzpKHc+KH86Gx8ufEKIWuGms9/hXszL8LIgmJ9sZk3x1hWoXMYjb/84cvWsT1yDginGR0uWv4LQRBHpM82WfyL79h9AfbZ9I6un4hixm3QEu+2ScYJQgB3bqkebG0jc8/4/tC1y3NswXVD6+IDyZw2/I4KYa0yvZkjWnt+/5LJ5xkJN8eEB1OkeHtlqym0+i6pTvbUP9yRreubJT6jtL35+x7l3o+AEIggmBodfSl4EWcx/OFct7Vr68f0Z3CdNqaX/z6mOThHJl8JCOTt81PSoLcCsj4wYz0m30qbeiWBSIOkHFI8nBO/vtHJI/3kGn4WXfQhJQEaYyZ5sTHtu7MdOqzizhdd3RnW9pFQdp0Q9+jL6l4+6b0tXIx02Hn3vYV29rUyvYO2dF/KNxxytimKgd5gtrWyCy276PfPGD0x8dk3x/b1gH3NAJ3mf4+N0C0Nyaaj2gXW9rFFoStjzNl+9lSaU3T0bxcEfx0Qv7bB1ZYS3GjXlX9McrcZgO0pxt7P59uPta0Pz8ushxOM5IHc2tochtDJmNsO5vFlvbM/ny26OxlXEpw34dRRsHQo1QEAaa1mR66adF1h64ba9zzAaKIMk8+XQnFHSCYZER7I9sH9bZ1lXeQL+dIPB7PtcgkIpjliDRCRoGtMEDbegAAIABJREFUnZrltoFv8HkX+yKQxA9mjP74GFU0VD++co19pySPbV+gYJx+kJ51HwIRBlYE//ahLZT/x6vPOh+1qSj/8dI6jf72gV2sfsUC7jwijoj2x6TfHJD97iGqaql/PqF9tf7cU/toiNi5VI5TxLIgPpoy+efvGf3pCfHh1NYVfSkRuMu4ZuLJ4z1m4rcAtC/XtJ22ac+fQcjpVtGebQieJbRnBapskEmEiG/+eRZkMcnjfbqzLcVfnn+E2d4dgjQiGKWEeyN7P9+k/+E5jDZ0pxtKl0Z5vk70s9LXWk8z0l8/IPnmwKUFj5GhhEA6AdfRPl9QP19Q/3RC/eNL67r7PscgBcnjfbLfPPhgh3PXkZnr+/poz5oDfSF4EefxfAXILB56qoWTlOTxHsE0s8YXMHwhGKMxncZ0yv1ojLY93Ix7nO1dJFwj4MClKMhbR35EIIkPp5hWU/7wCgJpU+G+PyZ5vLczZrkCo41LOVFDPZ3RrgbC1UIIIYZ2Aed3OofXv+G8RSiJDqdkdUf51+e72oXPhCpqmmcL6qPp0BtNxB8m0mL6mgxj7H2g7J+7f794rofH9jeLYdfHzy2wB6dMKRGBjUgMxeZSftBoq4wCZJRhjmfk3x+jywZdVDaC8YXWm/T94qL9MapsSJ7sWxfK3z4kGCU3q4Fz9/XufaaHHon2PjA2KnDFNQdh9xLctRdSDj2/hHOdFUGw+/17bjwMzxcQH9pel82rNeVfXqA7hVqVNgr2qVEata5oTjY2/XpRWDOZ2xicOKfc9uGccJIhouCtPTbvKzJPiI6mRHtjgnFy+4wRpWnPNpR/f2lTa+/IuRKhtG0yjv9/9t5rOY4sy9pcR7gMLQBQZ2aprv57pl9gXn5ux2xs+u+2rqoukZVJCRFauTpqLrZHAGSSSURARYD+mTHJJIGAh3D3s87eey3qNon/+AL+0w78fmuTZUb3NI3szRDemwGY4DCLBHqe0rzrjs+FcQ7/SQf1f//+m9nwWzvzymYMvu9OvFtQibiKim8AEQfwj5ulXXMDwdPOZxcRrjBQMwpCLQZzsmLOFGx+6e7HQ59atE5aG1MU2a6RycAucEazZtZBtmoQYRl98MfnCJ60f1WMOKVhlhnUdIXiYkbHmxYwmaIboLFk6x14ZFPda8A/asE/ae1c2WOcnCpdGYrOA7kRvA+BMxYmU9DTpAwXrsPrN8BvI8zb0eLd5go2KWCSfGMYYZKCRFGhqe1Hl2J6vQlQLvCZYMBa6AtOEQChBxEHm9Bx0Ywga6UBzx20uvDIR/iyB6sM8rMp+PsJXKEfZlF/x/DQg+zUEVoHr1unanafzv1dP/MmyWGWGfQ8hZ4nm8/A5ftfbvpYC1hsZtTIDY5vWsZE6NN73YwgmzG8VkwbDjvmfn0J+pzRdaT+f7wE8ziSv53CPOD77ZRGMZghfT1A9P0RZHP785MJARH5JNJ7DXjdOi3oH2H4t9eKEV01s9oRZx3UeIn0pwuoyXJvHGpFHCD8/hjx754i+v0zBC/7NM95JaeRZsAl/OMWeOTTZqqxyN4MkL8fw+U7ZiIyBq9bR/TbJ9+MczHjdO9hvvdgIyR3QSXiKiq+AWQ9RPCkDdmMEZy0qLXSk5v5KRgLWxjoOQmB/P0Y6esB8g8TmGVGcyXlwlw2IohGiPg3J4jSAu55l1zuQo+qcttWUhgN7DMp4JdCK3jeQfTbE3idj2/gHx2vMjCLFMVgjvzDGOnPA+QfxtCLDGaZkfDQBiLy6Yb5oofwRXdjdCFi/+Ng8+seNmelGYwrZ498mFTB6QdaSFkHV2h67z6QiBO1ANhCxG0qasZ+XHEr23nMKoOeJ9Az+qWmCcw8gV5ktJDPFKzSsMqQOCo0fb9xYJJfVkClgIh9apNqxeS212vQjnu3Tm1TZbVovRu9zezQl+CBB/9JG846JH/9gLQRQS8eqDJzx4jIh9dvQNQDwFEEhdetg4e/vnBxVxa3l4KcRLkaL6CGCxTDRTlbuKTPwjKDTXLYvHzP158fxjbvOfcleCAh6iFEPYLfb8DrNxEcN+Hy0jI/Dujrpdi0zN2kMreu9vnHLdT+9TlMkiN7O4JJioedjbsgEed1anDPu3SsWzxPel4+ZINeR+pgMI9SxMlWTG67vQZVbLfEOdpEckpDjZfI3w7J0GQ/NBx4HCB8eYT4X54j/O4YwUn7l18kqHLNew14vQac0rC6bLG8mMHsKuJ46fz8ovdNz04/BioRV1HxDSA7NcS/fbIxOOGBdzkL5xxUmZ+TvRkiK4Os9SyFWWZwhaZg1LJtbl39smmB/P0Y0W9PEE/JfCQ4aYPtsstV7tyH3x2h/X/9C+I/PINXVuU+1+6hpis61ncjZO9GKM5n0LOEjqu4uqAkgWOSglziLmYohkvkZzMSob89gdj1eMuKnH/SRjFcPPhCyqxyZG+GVNV80oZ/3Lr299pcwaY59DyFmq7F2gpmkUIvc9g0Lwfr1aYya3NNr3XZeosrLXewltbK1gGcwYp1qySHXghwbwl+Qa6pPAogYh+yGVGltN9E8LQN76h5eyHU5ftFM0UdRL85RvZ6gDx5fG6Va0v2tUClYOxr3uqdg9MWxXAONZiTNf5FeW7N0rICm19+BtS6CnfZdr1up7Q527jsMcHBpgm4L1GcTcEjH7Ielu1kTfgnbXJ8PWlDxP5OVvKffy1ChM+7yN+P4XVqmyr9Q+SrWWWgBnNk0QDx98f0Wm3ajLeDh17pUtmDXqSPz+CEMchmTPlwnRqwSyXOOphVBjVaQk1WVLEs9sfQiPsSXrdO+YbR9e5BoknCtvgwRvJIjIkqbkYl4ipulzJTi3EOMF7OnTg4+zBD5RWE1659Nj9sPUtWDOdY/uktln9+h/zdGHr6ZcdFm5BYWYddq3myuTl67RoZJ2wx47L5OsEozLsWwOvUIVvRL6swpVAoRgss//Ieq7+8o1mHayxi1l9TDBcUBAyQ62WZn8euuZq6eryidE6zmSKzjAeejcvejSCaEer/+nxTEdm4962P7eqMm6MqnJmn1JJ6PkX2YYL8wwT56QRqNIeermCzu1/8UKBxE+GLHsVfrF02fbmZnduVTTZh5MN/2kFUCtX8w+S2Dn9vkPUQsn79wf2rc4+20LCZQv5hgvSf50j+forV309hltQ6eauf73I+LnrVR/TDMcy/PCvFWwOCBYC8+aycrIWQtZDavrsNylYr26zvG6cNiuECjDHKKdOWugx2mAHlgQ//SRvBNKE54sdE+bmQrRjB8y65Uu4g4py1MMsMxZCuYWaZ3cHB7g7zJGS7Br/fAA+v5xIpGxG9Nr3Go3GXrbgZlYiruDWYlBBhDNlswT96AllvwmQJzGqJfHAGNRo89CFWfIKaJMjeDpH8/RTpP89RXMy2zh5SwwVWf3lHc2fdOvxjQNSD7W8yZVslGIMI/c9X4MZLZO9GSP5+iuTvp8hPJ1tXwMwyQ346QfZ2iPSncwTPujR3seUsFmMMohbC6zeQD+a0o/6A+xROG5oPHC9RXMyhhgtqYStjHOBoLsfmiizKx+UO9ZR2qanqlkEvUpgF/W7T/N5m/Zwy0NMEmQVMqqjK+/0xwu/6VKnZorL4JbgnERw1YVc50p/OH/w92wusg0moCpu9GSJ9M0RxNkVxPoUaLWBW68DsW36hyvZdNV7COQezylGcThF+f4T4d0/g9WhxexvzcrIZIfyuD5MW9OshKjLOwSkDkxZQowXyD2PIdo0qTVvCfUlt50/akM0IzJcbw5lDRzZoXlJ2atSiL8VO1UqqKC+Q/TyAniZf/4b7powXuNpC/NVvWc8Uy4fLS63YLyoRV3FrMCEh6k0EJ8/R+Nf/E8GTF1DTEfLBOZzWlYjbI9ZVGT1dYfWXd1j++R3Sny52yjxT5ZyMqEeIXvWpfSuUwJYijpUibiM6PnO8arLE6s/vsPrzOyQ/ne90czZLmpnL3gyRnLTAPAHZiIBtDTXK2Tiv34SIH3433GkLo3Po8bJsG10gKA0l6AscZcrNy+f+z3NkPw+Qvh6Uc48PNy9Ex282M3fZ2yFWtQD1wRx6laEhOPyj5uZrd3ZC9SkewjkH2Ypp8bRuAfzGWJ9TzlDFIj+bYv4fP2H+//4TZpnefeWirP6ts7vy9xOI+D3q//5d2QJKC3hXZoPt9J6X3yMbMaLvjsrq6xjmIRImXLnRkuRQowWyD2OEgu8k4lgg4fcbMKucnC4DSfmHhz7iyRhEI4T/tA2vU9tkp23D5nOtDdRwgfT1AHr2sFmen4OVEQPMu37+6VrEkbCtVFxFJeIqbhEmJWStDtlsgwcRuOdD1puwWoOHt+CUV3Fr2FzDpjnysymycufdZjsOSZfo8RKr//lADmqNiKppt4TNFMwqpzavnwfIb+N4pyukP57TLv3LPgS2nL1ijEwk2rWvmkbcJyYtqK2yFsDmCt48oYrbZAk1XkFNlh9V4swqh32gDK1fwymzaXekVkoG/4hMMXaGlVETgQfZjOH3myRgV49vNu5rOG1K6/sFkr+dIvnHKbI3Q5jlw8wOOWNgMoXs7Qiz/+fvUNME9X97Af+kTQv5G7SPiXqA4FkX+emEztUHrMA6Y1EM5nTtqUfAq/5uD8QYRQ4ctxC+6KE4nx3+bByjlvzouyMytdo1bsRSa7AaLZC/G0HP09s9zlvAGVu6vqYQtfBa5i1rkymTFpR3V/HNU4m4iluDSQ+i3oBstsDDCMz3IctWCBFVIm6fsLmi3e+zKdI3Q5oRu2F+jhovsfrre4hGhOi3txsiarPLFqT05wvk57dwvJMV8OM5gidtuB0WrYzRjJXslDl2bD9682xaIHs72uzUmnlaVt0ukJ9NkV/MaQFgP8l32zNcoWkub7qiihlnqOP5zUQcAAhONvylTbsz9tsUccqQQdDrARb/8RMW//tnMirR5kE+DxRNQZ/dYjCHWRXwe+RYyvgO7dlXELUQwXMB/22T7MUfMNvRaRJxzPcQ7irgABJxQSniXvaownfgIo4xBtmuIfr+CF6ntpuIczQP53Jdtt+PyZhrz3DGwpbRHcz3wK8xxmpzDVM6Au9L3l3Fw1KJuIpbwxkNk9AMnC1yOFXApAnUdAyT7d9O2K3AORgXEHENst6AsxZ6MYfN0r02czGLlCIE3o9hk/xWwmJNWpAF+XgBs0hh2jEFgt8g42eNmqyQ/OOstAm/neO1uaL2vXlK1ahcgXni+gYarMzkakTXd/+7B2yhoacrZCDxK+KA3AZHS+h5snu20ENgHVxORhvrdlv/uAURB5Qpt21Q+5X8JdkI4R9RJU7hIfrrHgaryZK+GMyx+tM7rP7nPbL3Y8qCLM1uHhKnDWzqkF9MsfzTOzgHxL87gb8W7zu0ka1z40QthNeOoRohTFLAqfvvP6R7RAp2MaOohnkKHkgy8NnSDIr7Ev5JC+G0f/gmPZyB+RJeu4bgWReiGe/0XjttoKcr2gCakemW28OqlUlKN+FmjJjzzTz4p8L1qvGQGs6R/OMD8tPJThuPFY+P/Vl5VBw8TmuY1QJ6PoXNUtiigJpPkQ8HMOkeDhbfAowLcN+H1+ogfPYSThVIP7yFMhpQDs7s55CCnqfIfrpA/n4Mk97Oot5milpYxkvoRQovU5u8ppuiJyskf/uA7M3wxm2Ua5zS0Jqy5swyg80UOOdg1z3cciecAr+9vfHIWFdY9CJF/mEMcA4Ys4lcODSctijOptDTFfzjFqIfjuH3KPtt14D5TTxEv4HifHbLR7zfOG3Kz8YEy/9+jcV/vi7nqfZkoevcZp5p8d9v4OA2mWg7wxlttkUBZLsO0YhgC/MgIg7W0byhddBj2liRjQhih5B7HkgExy3YtMDyL+/u4GDvDyYEuE+OjcGzDp3fO1TinDYoRktkHybQs2RvsyBtkiP7eQDmexSr8qRN1+rPOZaUJkDFYIbVn98hfzfcy+pixf1TibiKW8NpDb1cIL84BeMc+cUp9HIOPZ9BTw98l/ALcN+HbLTg948RPnsJk6xQTCfQyzmc1gD26waybp8zqxz5+QxqvCDnudt5cMA42KSAGi/JXc6XgLf7ZcZqWmjpWULHO13d3sKr3N00mYKaJfBWGS0cthik51IAgUcVPE9sAmYfHEuxHg+ySL0DnDIw1iE/nSD56wfg908h2zWwXTvsSlMa2amDXzOj6dBZfzb1PEXyjzOs/vIO2YcJuU/uY6UiK6CGcxSnU+QXM3i9xibjcls2lavQg9eluBUzT2EfqkGknNkqRkukP1/QTG493HpTgglRRrLUKJalXYPNilvb6Lo3OKOcyH4Tsh1vTG12wSoDNZwjezuEnu/v5jFteC7Afj7HIvKgp6tN8D0TAkxwigDSdmM0tPrre+RvBtBlRMVdohcp9GRJ3SqL9OArf8wT5HpaOp/KZvzQh3QrVCKu4tZwRsOs5rBZgmI0AJOSLkJGw6kDu6lcE+6H8Fod+P0TBCfPoedTiPevwYWE3Uf3qHIhZ5McajCHmqxgi9td6NusgBouoI+an82m2wanDGxSQM8Tsjufp7e+4LRF2Va5zMixEFssEgW5i3FfkpAzltpoK24f51BczLH881vIZoT4tycAdlvoMc7Ay8XvdTOaDp4yY5Ecad9j8Z+voUbL/anAfYIraPOmGMwp8uCoBV/wnUTcGh54ZQZlTHmWD4l1UKMF0h/PIWIKJd8azsBDH7IZU7xLv4FitDg4Ecc4h2zHCF924bVq1L2x4+3TKY3iYk4mPXtoaLLGKV1mcBYwswTJ308pz7DfhAg8MN+DzUmQbyI/xkvo6ZKMh+74vNWzhMyOfrrYe0F8HUQ9Qviqj+j7Y8R/eFaJuIqKX+BcKdoMUHwbRgE8CCCbbchGCyKKYbIEjItN6Pm+4ZSBzRT0KoNJcroZ3LYoyjW185XBujd6rLSAGi+gZwlsVtzJgtMVGnqZ0ozMFo+/CdHmpeNh6JfmDA9jDPHocY4qsm8B9fsFObRxTjbd255rnLIIRSOiavE3gM0UivEC2dsR8g9jqOECdp/nI52D0w56mSH/MIHXbUDUA3it3RdfzJe0E1+PHjws2VkLNVkh/ekCwfMuXTc43Te2mo0TDCzw4B81Eb7s0UzsDlExD4pg8Dp1hK+OINvxVq/BGmctVeyXOdR4ATWY0fz0vrKJnLBXnCozqOGcNgalIKOh9YjCZFmOLKh7ub+4QkFPVygupsg/jPcypmEbZCOGCD147dp+X/e25Nu4e1VU3BHcL0VcrX4ti+CHxuYKapqQw5Uyd7KbZ8uLv17cfB7BJDmKixnNNtzRzqNVFJJt0wLO7HZ3ZJ6AiHy6OWT7Mh33yHAoZxeLTTQC9wSY+Hww/K/BynlGUQu2Dnk/VMwqQ/Z6iPTHc6jh4mBsym2aI/swhmzFCHapVl2Be2X8ST0Ekzef1b0RzkFPlkiVQe1/LWGVgRACkNtv/nEpSMR9f4RitADe3MHx3iGMc3jdOqLvjiDbtd3CvY2jDcp5AjVeotjjKvNHWEemQtrApDmKiykJ2NJB1VmaEXVK00zzPd1anLawaQGTFIfZovsJ1itgUnoed92Kep98G3evioo7gkRcC6JWBxP7fzrZXEPPEphVdmcD37bQ0AtyfLypsYtJchTDBfQ8vbMbsivd+jbufFvCgE32GCsjNSoNdzc4bTbGHHqyAvdlaYCw5QMxcsITcUBzjY8YWvg56AWFvKevB9QadQgLXJSV/cEcRY8qK846gO0W/s08CVkPSbw/9PvuUBrKOMpsHMzhunXIRrT9bJzH4fUaCF/0kP54DiY5vU77MJ/7FZjkEKEH2anBf9Ki2cAdcIoqVsX5jO4XhzTDtYczzM5Yuifmio7rQK4XX2IthPfVrXRX9n/VWVGxr3BO7ZQNEnE4kEqcnlMV464uyk4bsuy/QWVrjc0UtVMus7sTccZunDV3XfQwwcED+egFwb5gkwLFcE7mJK14+417xsB9CRH5YFsY2RwkzpE50DxB9mZYBnrvcZvZJ9iC2rOL0aLMx7Jkw76TiBNXzCMeuBIHagGE0tRW+WaIiJHhzrbHxoSA16nBGUuGP4EHFHr/53N5WRFvRPDaNfi9xs7no8018vMZsncjmMX+zsIdCs5a2ELD6v2NStoGV7reOr37fX4feRQijkkP3PPBfJ9+lxJMSMp7ulKWptK0BSyZDzijYZWCUwpWFXBa7f5h5Zx+tlceg+fRMQhBO4acbXbnnbOXOy/GwOnLY7BFAXztwssYZL0JUW9sfoZezKDnMzijAefAPA/cDza/6Os4Nv7p5WtBuz/0cykW4Os3dx6EEHEdPAjAPR/gX7rous3z04sZzGrHIFJGYslrtsHDCM4YWFXALOcwyQpMemCeBxGE4EFYOjvJzU2e3nMDqxRskW9+4TpVojIHjochRBCVjy02n7Hg5Blko0mvMecQQQS/f0IXwCyF/aKhi6N/zzOYLIW9pxw9qpJlMOndOdI5bWGSAiZTNxaKNivbP1cZcENB+EXWO46F3vn8Z4JTW57gexP6/ZgxaemA2m/A3+WGzMr3bJ1j+IjfMltoajEbLlCMFtCz1Y03V+6VcpPFrOd404Kq3v72IoxJDh754KG3FyJuHUytx+RSKeohgqedrbyVAJAYinxyeOzW4fcaZNY0228zCsY5RCOCf9SEbMWUlbYlrrxm21yhuCARp5fZbR/qt4ex1MK5buM8dBxt2DptqkrcvsHDCF6rA6/dhdfuQtQbEGG0ES9g7GPBVBSweUpB1PMZ9Hx6KYKsBdz2bzCXHmSjBa/VgWx3SHBEMUQYlwt+sRGSVms4rTcLeLOcQy9mULMJ1HT81QU9ExLh0xeIf/MHiLgGEdWw/NufsPyf/4JJUzijIaIa/G4fXqcPr9ODqJWiq2z5c9aSaMxSqNkEejpGfnGGYnTx1ecqW21EL38Dv39MzzP4/IWXyvEp9GKO1T/+guSnv2/9uoJxMCngdXpo/PHfEZw8g8koQDz58a9Is9cQcVza/J8g6J/Q6x7FGxFviwI2z6DmU6jJCGoyRDEewaYJfn3lxsA9HzwIERw9gX90AhHF9NkKI/Aggmy1IevNzedMttqo/+F/IXr1A2VzfeGz5IxBMThHMThHdv4e+fmHe9ntcsrArnJq47mjC7PThly1it3aE69iCwUzTzY78HeBM7Tj6LTZLAi2hQl+a8HmFV+H5uKWVFHe9XPBGZhgmyzDQ2k/2xaT5MhPJ8jPJtRGbdzh7axbV5pWZNDzFLLFdjKk2VTMA7lTBtmd4AA1WSL95zmCp53dr3OMgXkSXq+B6PsjapvddxEnOfxeA+GrfukMvCPObURc/n5Mm34VN8JZB7fOUjy068XncI6EqbaP6jp/0CKOByF4EMHvHSE4fgKvewS/04OoNyGiCMz3L50C186JWsOpAiZNYNIV5GwCNRkjP30Ls1oBUHDX7UBgtMgXUQzRaMLvHsHvHsHr9CDbHYiIBBaT4iMR57SG1Ro2SzeCRI0GlFm1XMDiK1UZTmIhev4KotGCbLSgJiMkP/8d4PSz/O4RgifPEfSP4XWPSNj6AZiUABicM7BFAZOsIAbnyIWAWlwv9JZ7PmS9Ab/Tg987Ag/jstrISThxQb87C52soMZDZGfvr/mifvoaowxprSF4+hzx97+HSZYoRgN63WZjEm9HJwiOn8I/egoRRRBhRO2NXFClMc+hZhMUjRZEGANcQk/HMFkCp4ov/2wpIYIIst1F8OQ5RFyDjGok4sLosvLLafHO/YCEs7O08/MFkegM9evbIoeYjnd7bXbAqbLVMVN3diFbtye6/OY7eJeVw+LujrdcIO7cNrL+7EsBxtljLursDTbXZM6T79Y9sZmnWr93gq5XXzpfDxmbFMhPp8jPZ7BpcbALMsrLymDmKUS0WywE45zmKPdsw0UvUrgPFDlgkpyuJeX15DqsP88bUfT9EdQsAV4P7/KwbwyTAl6vjvBlD7IZ7fYgxtKs9zwlV8rx8lG5Dz4Y1pI7pjKPQ/Q4uoY8mudTctAizmt3ETx7ifD4GYLjpxD1OrgflotqVr5pCgADKwUGCwLA98HDELLRhNfqwBw9gVMF8ovTsof8GiqOcTApIRstRC+/R/DkObx2F7LRInFZCiaq0JTfwjgN4DMOISW450HEcSnwLPRqWYqs7WFSlJllXfi9I/j9E/j9E8h6AzwMwYRXzhHwcqHJwcqkXBHF1Coor9fDYfMMajoGkx6sUlT1EmJTtaJfwUbY3CaMMzITqdXhd/uwaYLo1Q+IXnwPHkYQAQ1FW23IcAIMTHoQQoJJD7Le2FRL89N3SN+/hv6iiGJgXGxaU0VI7ZTWGCDPaEMgiiHjGlA+V6d1KQzLKtQXFkzOaJjlkloq9f3dcKw21JJ0l5W4sj3xJjNmm8cqDMwqo+O9q8WntRQqru3O6ouVMQO4Qb5RxfX5uHp6s8dinIEJQefDno8Q7YJJcxTnM6iL+UE7zDljYVY59CKF7OyYP8noPGWeuFKB3f28vy3I4MSSwclwASY4GZx8cVTh8zAh4PUbiH44RrbnAg6gyqjXayB82d85t8uWFvzFBRma0L3t8bTLPRRUibtZh8pesSnkmMfRHlpymCKOkSDz2l3Er35DFZjeEQASGDSLVNBCWis4AHw9J7f+vZxt4lFMi/QwxDarL+55kPUm/KMniF7+gPDl95BxHTwINz/bZtTa6MoA4LVtLLX58cv5KlbOqu04rA0APIg27aThs1fkmBjXwBiDzTI4u6JZOwdAiE3FjKqCBYnXa174bJ5BTUaAMTDJkkSb9MADEsay0YJkbYhwN5epX4Vxmn+La/D7J2CMITh+Cq/dhVUFdLLazPjR7Fo5GxgEG/En4jq9NkJAL+ewaUKza7+YRbyco3RFDp2sPtrBZ4zB6/TAgwCiFN+2yKGmY5jVkkTeF+YbnTVQoyH0fEZm0ME5AAAgAElEQVTzefdmG2zIZvcO8uE2lJUtV1a21jeAbdzk1o56ttCwaTmvdpeVOE3uWzvfrDgDJL/2znnFzbgUcTf8DK+vyYKDafbQa/lbxZWdHyYpKDB7tIDND8ix7xPWFX6T5Ds769L5edlCS7Pq7MGrk05R25qarJCfTcnsI97eQZMJBtmKETpXBtl7pZnDnomaciaVRz7N8J20dpqHA6gqXwwpEN4s7s51+VvDGQurDEURPQLRQ8YmVIl7TCL/IEUc96ni43V68I+eQDZaAOfUtvf+NYrxCGa1gM3zy5kkRhdtJj1yFKw1IOLLbC81HsIW+bXdnERcR/TqNwhffAf/5Clk+Vi2yFEMz1EML6CXc+jFHE4rWK3AQAYnZMQSQMQxRK1Bc2nTEdTwAjbfzTXM6/RoRi6K4bU6sHmG9M059GJOQqXIgbKFD5yqZiRkJPRqsZXxiMkyYDKEWS3Ahv7GREZENXhlayvz/LsRcaCKJvdDBMdPIOMaTJZi9c+/0bzbdETiyZiN2JfNFlUoj04QnDwD93x4rTZsdgJ1MoZTioRX8snzdw4mz+CsRWINivHwk6qqQO03v4fX7ABlBdAkS2TvfkZ+cQaTpWUl+DM4VxqaZDDZ12bzbg+nSxMPtbuJx/V+EMqh/TLXZktt44wpFzb67nfOHADjbpjBsw6nrUTcvWAsXKE2GwU3gd623TfQ9hZjYbWFTXLKbZwndN4fKs5RxVzdwvVgLdw5L01e9mORqqcU/i3iAP5xEwi2dDhZ5x/WI8huHf5xiwxO5sm+PEUAJOBEI4Lfb5ChSS3cOfLBZgWKsymyahbudllX4pR+JJW4UpiaRzLjV3KQIm5diZGNJvx2FzwM4ayFmk+RvP4nsg9vywrHJyc0Y1R1CyL47Q5ks03OhkKgmIy+vOD+9DGEhGg0ETx/hejVb6hl0fNgMjLxyE7fbcSkmozKylyx+X6qDIUbIxRnNPRyTvNwajcRJxstROu5OwB6MUP69mfkgzOo6Qi2NDwBaMZsbY3Pw4hEZpHDpKtr/SynCujPzJGJuA6rFbjnw++f7PQ8vsYmBNPzyFTF81H8829Ifvo78otT5IMzqjBtTlIGr9OF3z+GNQqi3iDzm7gGr9tHcPIUNkvIYOZTEVc+V6MKmsPD6eU/cA4mJGSziZq+fC1MliK/OEP65p9UFbyG2+e9Ygxc2ep419cx50qjCOvKKvMW31vOOVhlKFrgTgWnK6vluxs+MIYrz/GRiYE9hKrj+nZiMth2n81DgSpXBfQqpzaz1Z5di7bFlaZJ6vpdI1+E0XwcVWDN3ugbPUuQvh7AP27CFk/Ao+0y8ViZfwgAXqdeGqU46EW6VwtXJgW8ZkyulM14pxlHqjSTU21+PkN+OoVJDvwzvgesBRtV4h7RTBzcFXfKx/B8iMMUcVyAef4V50lLi+1kBT2fUhXOfEaQlTkRFinUFDBJUppxMOjl9apQ3A8g6k1yfmy1Ictqls0yZO9eI33/BsXgDMVosLHs/6i6tzY2cSl0aUPvnKWvU2rncGTu+UBcQ37+AdmHtygGZ8iHFyQM84yOYX1yWgtbFNCLOVia0OtnNNwX7fD3D2cMOXpORsjP3iM7fUci7BdzaA4mSVAML0g4x3XAGGqD9AMEvWPYdIVidIHDefa7swnw1PdwYd7EWNhyPnKLdspNxfAeds3c+j83+DmMgfHdAogrdsC50ifqJtXTKzzCIqrNNfQ0gZmnj6LFzLnbW4Stu2JuMsJwF+hFCrwfo/jhGCYtLitU2x4iY/B6dUS/OYFJCxSnk70y7WGegHfURPCiB9nYsWPHOlilYZYZ1HAONZzDpt/CXfyOcaAYLmOBtR3/Hm0A7IrbzMRV7pQPD6d5ss3Fd21MoApqUVO/4sBlDVxhoIsvOBJ+7Uf7AbxWB36nD69BMQKwFjpLkH14i9Xf/wy9mH+2qrOG5uQ0tTgu5zsdx6cwIcBZAL1cYPXj36DGFzDJ6vOi0JHNqrlHQ41bxxiYxRzFxRnywRmKwdkXv9TmKWyekqtmXAf3fYhafVONM+mKHCu/AV/B9SyRVfcwrFy2UzrnwLb8URRTQGLzHoIXSg13ExGHR1vR2Usc6Eb8uE/XG2ELBTVdkfOhOnwRt96EvZUNqHXlfMsOgbvGrHLKQBwuYBYpbCOC4Gx7gxPO4HXqiH5zjPx0QoZLt1G1viW4J+H3Gwhf9CAau7lSOmNgMwU9T1GMFlCT5aNanD8YrhwtMGUXzGN5Ta+4U1YzcQ+MK3KqMGUZnNGleYUPr9tH/P3vIMIIajKCSVew+nYNHJjnk7thqwPm++RGmCxpHms2ofmzHVsib4LJEujVEmo6uZwHfEQf1E9x1kCvligm1Cp6HUyeoRgPIZttBCfPS7tp/zIuIAjIKXLHaugh4KwjAafvYXetNFbADm2KV90H73qhTvrt5oKA7dNqsOKbh0K+U5hltnOHx16x3hTSNzAguso+nq7OAZbaKrO3IzBPInjagdh2XoyVBicAvG4dPPRg1wZODw1jYIEHr99A8KwDUd+tEmeSgvIPTyewSf4oqkX7gDPUqm4fWdvhZmxC351J2kNwkCLOFgWcW8JkCawqyB3R8+G1e4i/+y1Z5TtHpiZZRgvz9Ql+wxOd+2SK4bVoHssZDbNaQE3H0PPJtc1BbhubpVCTEfR8Ar1aUJXvEeOsKV/3EcxXwtHXrF01VbcHqwpqy/VFKeIoFoJaS/fgRndXlDdymjO7+x+3DlDe2tukvJHcj9jELQq4fVwZVnyLuELDzBMK+N43d8JdWIf13mZ1YN9O17IjQM8TZG+GEPUIfq8BhKXByXVbPxkgGxFELYDXrUNEPlUgzN1vin3tuCAYeFiKuKcdMG9HQ5OU8g+L0ylMUlRV+W1Yd54YC/fJZ2rTBVM8LgMQOEftoeVM7S6u2fvIQYo4Zw2gChSjAVY//hXByTME/RNqdez2wTwffqcHvZhBrxYwqyX0agmTLGFWK1r0u90Wh0zIsmoTUraQMTBJAr1ckE39A2GLAma1hM2zx3XifQnnYJXaVGOv9S1Gl62V+UfVWarIBeBhCKvVl8O/HwHO2tJO32Cf73rrAO7HXE2uqLhLrDKb9jy3R610FV9Hz1Okrwfweg3Y3x4D2KHlsIwz8jo1RN8fIXs3RnE+fVBBzzwJ2Y4RnLQg6xHl9e2YJ2uSAsWHMfLTSWVosi3OoTifYfXnt7/4J4r1KZC/Hz2u0PR1JX9jYob928TZgYMUcSjNEorhBVm9OwdZb0LWmxBRBL93TBbuaQI9m6CYDJEPzlEMzlG4c3KLNLs50TEhNu13TAhYVcCkK5jlnGbxHgirCpiETEzuLP9rnyhFnClDt6/1LdrAuKxsNb1SbWN8E1TO0uSODnhPWIuje6rE7Yqz7vG1c1RU3CNOG5gkh02LajPkwNDzBNlri/B5d/dsv3JOV3ZqCL87IoOT4Rx4QBHHfXmZC9eIdo4VAACb5pftlOnj3Xi9C5x1KC6mWP3pDT5VMrbQsLlC/n4Mmz0iEQds5v3WHUL7NhO7C4cp4kpsmqAYXQCcwSQJvHYXstmCrJcZcFxA1BvwpYSIavC7fejnLyk7bTGjX/MZ9HJ+Ob/zNRjbhHSDsbLPVt/67N3WrJ13vpGbNRkK2o/K4l//JleGmq/bBK6U0zkH4+L67SoHxjr4dzP7te/aaO2OddfxAhXbUYb0MinAfAnuSdpNlwJcln8vOSAEmKBKADijkGVWhqHzMlOvdAZc//mjv+O4DOL+zL/LRgTZjBD9cAzuH/Rt7M5w2sImBS3EqkrcQeHKKqqaJigGc4haAFELr/1ZX7eIOefgtWuIfjiGGsyRCP6gl37uS/hHLQTPuhD1YKdWNpsrMn8Zr6Cm63bhRzwCcRdYh+JihuWf3v5Cw6wdYPUsgS0OOFfyS5RrZas1uBRbmwbtGwd99zNZApOn0Ms58g/v4PePETx9Qe2VR08gG02IuA5Zb8L1joDyjTNpgvzsHfKz90jfvSYXR3u9/l/GGM1SfSTiDDlOPqSAWs9y2X0yEr5Dyp7u7exvy/ksZ/GpiqHFJn+85hRXMtsOIrhzPbv3jWxKHAyMgXkCPPQh6yEtLCMfIvLBQw88LH8PJJgnwaUA80pxJ0T5OyeBJzggyqyu9Z/55f8zwT7zd/R19Ljl4+04U/PYcdrApDnNt1QV7YPCKQOjDfR0heJiBtmKwX0P2GHDwuvUwDhD+s9zOn8eEB54CI5bCJ93IWu7GZrYXENNVyjGC+hZQq2U1cd7O8p2SjX8gjv6Or7lMW7+XI0q4fzgV3wHLeLW1TNb5IAxKMZ8kx+WX5xC1hoQtRpEXIOIahBRDO4HkPUGcPRk00In600UwzPkg/PrCYKrjntsvcPMqbXzIdnkXX0DlK0i9LpvcRpu3qdPhnmtKyupj/T122S2Hcbz2wSFH8bhHj7l+cQDDyL2SYxFPkTogQeXv1ggyz/Ly7/3ZVmV+1hYbYRXKcKoIreuxl2p0H2uUvdJxY6xq19/5ese+nXbYzZxIsXjcmP7ZnCUG5e9HUE2InjtGkQt2PpheOBBtmJ4nTq8XgNwIOFzn58JRps2PPbh9Rvwj1rgOwR8A3Ts+YcJivMptVFWH+3dsBbuEWq0r+Gsg00K6FkCEfpwgffLr8nVFsWBh+WwRdwaa2GdgptPoFdzsPP34EJCxDXIVgdet4+gfwK/fwyv04OsNeB1+9R62erAPzrB8s//hWI4+GyV5iqubMmjyh027ZUog8cr7gcGlItDQS1W1/qmsmWS84/fK+coP9DeQ3baA+Esyjm4A2pPXG+UHMjhHjTlhoioh/D7DXjdBrxeHV6nBtmuwWvFEC1aRPKABNtH7Y7rjLzN/+PyHGOleyf7+Odd/nnzH3z0Zb/4mk+/oOJXsWVMx31kQlbcCWaRIXszhNetI/r+eKfHYL4H4UnITg3+UQsu12V19h5bEDlV8EUc0rWl3wT3djQ0WWXI343IlfKxzWxV3D3WwiQ59HQFF+nPbibYSsQ9AGVbI4yBQwELwBY5GX5k6SYGwOt04bV78Ds9iEYTstECkxJetw+v3SWTkjTFF1eOxsDmOWxB0QaMc/AggohiijaouB8YK/MBAzBxzTkBIciF0g9IyGE9N2DJ6bLIH2+8wGag96EP5Jqs2zkqDXe7lOJKxAF47EPWQoh6SDM3cQDZjCFbMUQzIovyeghZzuOIWgAeepuWyIr9xpnSxOg2wrErHgST5MjPpwguZtDLFF5RL2fyr3f+bTZUwOC1aghf9WDTAmq6utcA+I2hyXETshGC+1tsvpa4clPPLDLkpxMUg/njck+suBdsppD9fA6APpefmzNNf7qAPZA5y8cj4j6DVQXcYgaTrKBGA2qdbDTh949R/8O/Ia7VwX0fTEh4nR78oxMUowFMnn3xpuesgc0z2DyFi2tknhLFkLUGuFeJuHtj7SgZRmCrxfW+Ze0sGoRUkVtjLZzKYbPsUQd97xK6/XBUAu5OYAxMCsh2DP+4hfBFD8GzLvyjJrx+AyLyqTVyY06ybolkm5bIquPgMKCYDk2zHwdz3ldcxaQFrDIoBnPoeQqbKfCI7bSJIlsxou+OoCcrpK8Hd3C0X4aHHvyTFoKnbWoJ3VLAAaCNSG2glymKsymKwRyuqERcxXaYtEDyP++R/Ty4bNn/9GuSnHLyDoBHLeLopNcbC3qTpbB5BmcNguOnMMmKFvWeDxFGEPUm+GKOX+vXsUpBL+cwywVkowUexZD1OrxWB6LWAA9COK0eb0VnT2CcQ8Q1eK0OzHKO61zKuR/Aa7Uh600wKTfB3rYoSJhvU4lzuGxNvLI+YlfcSxlj+yNCDq0tcXO4h3TQewhjYJLaJGUjogpbK4bfb8LvN+Aft2h3vE1tk+TW9fkbW8WB4RxV4w5q86biI8pwcz1PUJzPympWC9zbfukmmhHCFz1k70YQkUdzcfdkXMFDMjQJnnbAazu6UmYF9DyFGi2h5ylMWnwbcUoVt4uxMMsMZpk99JHcCo9bxH2KNbB5CrOJGJhDcqroMOlBhBGY5/3qzIVTBfR8CjWfwu+fgDUkRL0Jr5PDa7Uh4hpMmsCZ9P6e1zcI4wKy3oTfO0Ixvt6uIg9CeJ0+vFYH3PNJxKmc2m0zEnHbOV3aX1ZsGQcTklo8924hfGBCruJmMIAJBh75CJ51Eb3qI/z+COGrPkSN2iR54H1kSrJ/n9mKnXFfuEZVHBxmmSN/P4JsxZCNCKhv7+wo6yG4FPD7DfAoAJcp7NpA6o4RgQ//pI3gaQci3t6cBQDMinLh1GB+KeCqj3bFN85BijjuB+BBWDoNMlit4VRBNv/mV6ICOAeTHpgf0O9CbBwlqSrz9aw3W+RQ0wlkYwSdLCGbLTBBQs4/foooTVCMh1DTMZwqKAD80ww6Rnb2XIjyODjAOJzWVCk0jzCb47YRnDIAe8fwRwOo8ZCEWJ7+4v1nHs3OeZ0eguMn8DpdEnGqgJrPoKdj2CzdblfPubIYp2FVAasVGBfgngfZbEM225vK797sgn9L7qXfMExw8NAjodapwe81EL7qIXjRR/iii+B5F9z7/CxAxePC2SvZkBUHi0lyZO/HkJ06wle9TXvsNhUt5kkIKSDbNfhHTdiUcujcXWaBcWrfFvUQXr8Br1sH/4wb4HXQywz5+/HlLFz1ma6oOEwRJ2oN+N0+zbNJD3q1hF5MYZIEJkuBz4kgxsClB9lowe8dkUtls0UmF3BwRQ6zXHy1GmPzHGo6gohr0PMZTLtLrZhhiOjF95BxHen718g+vIWeT6Hn00txSQdC4tHzIKIaZK1O5hxSwiQrFMNzmLQScV+DKnENMM4RLGYweYZicIZ8sN6huxRkIqqRgHvyHMHTF/C6R2B+ALNaoBheIB+cwaTJ9gfhaObEZilsnkOEIXgYITh6Apul0KsFTLL88sbC2tXvukHzN6Bax307sEDC6zcRvugi/sMzRN8dUbtkK6b4gMAji/6Kx836ulJV4g4es8qRvx/D6zVgk4Le023P4dLUSDZjhC97MGkBs8ph7lDEMSnIMKkVw2vXIJvxzrmOZpEifTtCfj69W+FZUXFAHKaICyN4rS5EowEZ12HSBGo+hUmWMFkKpxTg7Me7VVxA+AFkqw2/dwS/06P5NUNGJXo5p8fIflnJuYozGiY1ULMJ8otTiDCE1z2CrDdIFIYhtTFJD3o2gZ5PYbUqq2tsIyaZ51EEQq1BNvkA1HgINZsAuwiKe4CtK4dS0u+MX7EZB0S8ng2sg0sPQJkL5fnwGi34/RMSPs6htB/c/L9TBWyeXzt0HShD1q2F12wjfvUDRBiBB1EpmnXpzMXhtdrwukcInzyH1+6BBwGcVtDzKfIB5QOabLf2V5NnUNMxZRBKWbZs9sgVNc/oM6Yu7Zw3duwo3VMLMlSx+X30Z38jQfDfKDzwIGoBvKMmwpd9RD8co/aHpwhf9imA+9Aqb279m7usIjt8fH3gV2IOKn7J+lpbnfkHjc0V1GQFNVxATVYwqww8JBOi67I+R2QzQviqDz1dIf8wBlb5XR02eECulF6vAdEIwcPtq3DrYGY9J0MTNVnCqkrEVVQAByriIARYEMDv9OGfPKV7e9nS5pSC0wpWq8v2OM7BpQfueWBBSJWzWh1gjJwrp2MUowGK0RC2uEb7m3MwqwXS1z/CFTniHwyYkOBBABFGCE6eQzY75HioChIbRtO8FFsH3wr6Hilpwb9awqqCZvL2FB5GkI1mGaLeAPf98nkIcukMI8gGZe+JOKbFledD1uoIX3wH5nml8CqjIKyh7DJrUExGVIXcOET++nvgtEYxGkBNx5CNJuLvfofg+BlMuqLXXKkyR06ChxQBIeIaREiiSs2nyAfnyM/eoxhdUDvlDpjVAtnpOzDPh4jrEHFMLbaeB9lswaQruKIgc511OLmk0y6/OEN+cYZieI4iz7/6nCsqvghnkK0Y8e+eIPrNCaIfjhE860C2YvDIA+MHHAngANgyrN5YMmNYx8p5AmwHk4eKioOizPzTixTF+RTFURP+cXOnjRnZIBFXnM/Ag7s9d0QUIHjaQfCkDbFjuLctNEySQ01XUOMFzDyD05WhSUUFcKgizlo4rSjvKAjJMl5cDuU7Y8iV0tqy+MUp24iLK5UfBT2bIB+coxicoRhewKwWuO5C2mQZ8sE5nDHgQUBtCo0WRK0O5nnwwx4gOBjnpTuYLXeMOQBHOtEaOGPI7XJHEXGf8CCE1+7C71I7Kg+j0sSDhByXPngYggdk4w/GwIUEyhZDEdWoSlY+bxhzKXCFhF7MSYCtg9R/Bac11HSE7PQ9Iv4KstmG1+7A7x+Vj2nK45K0gGWc4iGyDGo+RTE8R3b2DsV4CLOc7/yamGSF/OKU4ivqDXiuB14KV1lvAM7Rc9KGPp6le6WzVCk2yQp6PqVFaaXhKnaARz5kI0T4XR+1Pz5H/PunCF/24HXrd/6zXdmudymwyj9bMtWg3y8r7vRnXKnGo/w3XG6euStfs/53Y+l6biyctmABzfSt27Qgd2vRqqg4CBw2rnr52RRev0mOs81464ficYDguEWRIvUQzF/dWZYgj3z4T9rwn7TBwx1FXJpDjRbQY3KlrLLhKiouOUgRp1cL5Ben1NbHOWSjCR7XyPBEelQd8rwrJpO0eFi3rq3bL9V0TFWQwQX0YoqtVtHOkjHGbILVj39DMRrCK9s0ZbMFWW9SBag0YFnPPjmrN8LFZCkdy6Q8juEFbH53rQ03hQchZKsLv38C//gJRBhftjIxvqkyrcUTgLJ9VALl7J8rbfmds5e5Zc7BpAmyD2/AEgHH9Feroc4a6PkM+dl72DxDMR3B7/ThdftloLcPVlYJnVawBUVD0Ht+gfziA4rRsBTuu2OyFBgNKE7AGgT9E3jdPn0mg5BaZ6VHr4e1JCTLSAOzWsEkK6oaV1RsS7kp4B81UPvjC6rC/XAM/6S9swPc1lgHmyvYTMEkOeVaZQo2K2ALDZcrWGXgCk2/6/JXaZ2+/rURf4auC5eijf7+UuzRtdzrN+D3G4j/8IyCyCsRV/ENYNMC+fsxZLuG4Flnp8fgngBqIbxWDX6/CTNLoWZ3Y3AiYh/Bkzb8kxb4jpU4PU+RvRlQLtw9BpRXVBwCBynibJpAWUvVN+c2okmEMVXlPK+szFHVC9ZSq2VRUOVjMUcxukAxHqAYj6Cno+0PwjkSYqsFGZJMhvDnE+j5EdnYt7vUvhfFpbApF/HGkKhQCiZdQS8XUJMRivEAejqGK64h4pzbVJScMdQeuJiRG6JWd2iSUQo2zsHWEnkz42bhDABVbPmQ7LLNkEpV1/o2Z20pgEcwKb3+5gm1pMq4DlGjIHYwBpvnMNmlWKbq6zlMssRNy19OFdCqgLN20xYbZOnm/adgcQ6AURWydCA16QpqOoJefd1Mp6LiczBfQsQBguc91P/tBeLfPYV31ITcwX78S7j13GpZAfuFAFMGZpXBrnLoZQa9zGBWOWySw2YFTFbAZho2VyTkCg2nDOxazH30mGV7tf5E4H2mQhC87CF61Yfs1hGbp7f2fCsq9hmTFcjPZ5CdOswihdUGjLOt2qWZFORS2Yrhn7ShJgmZnNymiNu4Ukbwj5rwe/WtWzfXngYk4kaViKuo+AyHKeKMhstTFKMBTJKAB0GZ9SavtM9dGkhQBYyqIE6VYi4jJ8tdZ6E+ojTmUNMJbJahGA2pCuOVJiBlpMDGyMOaMqNMkT19nm3s6O014gWcMchO38Lkafm8fZhlmX2XrO4sokDPJ0he/4h8cL4Rp7eFmoygZlO4dSTDdWAMYIBJE6poZSmKizOKFPC88v1nG+Fs8gw2TWDShGYfb7F/0RY51GxSRlCML6tw6zZfxkjEu3LRqhW5V64dUSsqtsTvNxH/9glqf3yG8Luj0r77li/pxsIqA7PMoMZL6OkKep5Az1LoRUqCLSuoGldW3Jw2l1W3UvytQ4s/12p52WZ5JZh6Y41fbW5UVKxxhYGerqBGpcHJMoOIfLBg+5lXUQ8RvuxBzxKo0fxWw4954EE2Y3jdOhmaBDvM5ZbVdz1LkL0dQY0WsLoScRUVVzlIEbeepdJFAT2bPPTRAHBwWsMs5zear7o21qIYXqAYXtz9z7qCWS1hVst7/Zlfhm0qdzbPyNJ/PsVDySGnFcxSwSwXAM4f6CgqvgnKXW7/uIX6v71E/IenCJ51b6UCtxZR6yqZyciGXI0XyD9MUJxNUQzmKAZzqNESepbAKU0Vs4qKijvFaQOzNFDj5WZThXG+U/aaqIcIX/Sghgsk/zi71blsEfnwy5ZnWQt3Or6NK+UsoZDvSTm7V1FRseEwRVxFRUXFN4psxfD7TUQ/HCP8rg+/37i1+ABbaNisQDGY08LpYk67/rMVzCKDWWUwqwImzWHTojSQqqplFRX3ic0V8tMJsrdNMF9CNqOtH0PEPvzjFvyjJmQ9QOHJWzM44bUAwbMu/OMW2A6xAgCo+j9dQY2XMGkBp74+K19R8a1RibiKw+X6I3QVFY8G2YoRfn9MIu5lD17n5i6U6zZHk+TQkyXSny6w+ss7pD8PUHwYQy+zyj21omJPsFmB4nSKrFODf9L6OBP3mvDQh+9LcqlsROCBB2vdJtP0Jog4RPCsQ4YmO1ThAMAsUxSnE6jxotwwqqr9FRWfUom4ioqKikNAUMZkcNxC/V+fI3zV33mB9CnrIOHszQDpzwPkpxMUgzn0NIHJdSXgKir2CJtrFBczyFYE84dngLaAuOIDcB0YAM4gaiGCZ91N26JZ3EDEMUaZlfUA/pM2vN7uXQJqmiD9+QLFcFG1UVZUfIFKxFVUVFQcAIxzcF/CP26h9q/PEZy0wG7YRrlxgJuukPzzHMv/fI3F//4JepFWwq2iYk+xmUJxMYOIfeh5WuaQCmAL7xBWGm6JeoDgWTSedcUAACAASURBVAd6QjN2ZnEDgxMGMMEh6iGCkxb8fgNsi/gPd6VdUs9WSF8PoEaLaua2ouILVCKuoqKi4gDwOmQJ7j9pkyOdFLhJP7FzDsX5DPnZFMk/TpH89RTZ+xHsoVTetogkqah4XFDsh0kLFBczZO/H8PoNCr7fEhGHCJ93oSckmm6CqIXwOpQ/xyMfTPDtqoMAxY4oAz1JUJxNoWdpJeIqKr5AJeIqKioqDgDZrm3CvHkcAGJ7W/FPyc+mWP7XG6z++h6rv36AzYpbMTaoqKi4QxyuiLg58g9j8NDbTcTVKGtSjZYQOwZyX30s/6QNr98AD/2drlFOGdisgJquSMSt8uqaVFHxBSoRV1FRUXEAyFaM6Ptj+EfUorSNicFVnHXkMrnMkL0eYPXX98jPpnC5OpjFEmMM4ByMV5W4im8Xm2vk51PI1zV4/eZOj8GkgKgFkO0YXq8B2Y5hkgJuh/BvUaOqnt9vgvty+2uUc9CzFfLzGdRkCatuxy2zouKxUom4ioqKigNAtmqIvjuiBZK8QRXOOeh5iuJsgvTnCyR/+wCbqcNqWeLsslWr0nEV3yi2UCjOphCRj/j3T3d6DCY5hPAhWyTivE4dzsxhdhBxsh4ieN6Fd9TczdDEAWq6Qvr6Amq8rOJLKiq+ws37cSoqKioq7gzmCYg4gGxEkK0YPPKBXSpQrgzyNhb56QSL/3yN/N0Y5tAEHEqTF0+ACV5puIpvFqct9DJFMVqQMcksgc3VVo/BGAPjHCIOEDxpI3jShoiC7Q6EM7pONSP4x0147RjMu76hyQbnoCcrZD+RiIM9rOtSRcV9U4m4ioqKij2GBx5EM4JohBC1ADyQW5sFXMUZi/z9GLP/75/IPowPcqHEykUjExxVKa7im8VYmFUOXYZiq/ECJi12eige+vCfthE870LUthNxTAjwwINsRBQe3orBdugWcM5BlTmVuqrEVVR8laqdsmK/cQ7OGJjlHOnPP8JmKQDApAnyizPYPIczVYZMxeOFRz68Th2iFtAsHN9t7805B5sp6FkCNVpAXcxhiwNxovwE5gnw0AP3RKXhKr5tLJ3XxWCG7O0IkRA7GZzw0IN/3CKDk1oIcA44e63rg6j58LoNeN06eByAeXKr89JZB5srmFVGmZXjJUxaGZocHGVOIHDYl2Xn3MF89ioRV7HfOAdnNIrpCOZP/wH+jz/T3xsLkyYweXqQlYSKiusiIh9er14urG5QgbMOepGiuJhBzxLasT+QG9WnMCkoZsG/WVWyouIxYJVBcTFH+tMAslNDiP7Wj8EDD36/CX2ygqiH4JLDapCQ+wqyHiF83oXXq++2seLIbKkYzKGnK+hFVgV8HxoMgKBZ5XUG4cGiDJw9jM9fJeIq9h/n4IoCutitTaSi4mBhtEMuWzWI0LvZ9qa1MPMU+fkMep4CBzYHdxUeSMhGBB56lUNlxTeP0wbFcAERDxH9cAyrDLUcb2HxzwQHj3yIZgSvW4fs1KHnCew12jNFI0Lwoguv1wDztneldNZCT1fI3o6gJis4ZWiGt+LBoa4HHzzwwAMJ5nvkPOpJEmyCnILXZlNrw6lDvipn70fI3wwPYiOhEnEVFRUVewzNmoQkWG40C+egZgmK0wn0MrvFI7x/eOBBtmLKtTrkHd+KilvAaQM1WYJJDjVdUlyIL7cScWAk5ETkwz9qInjShjPmmiIuRPC8RyJul1k441CMlkh/voCerHCQPd6PEcYgooCC5LsNyG4dsl2H7NRoEy3wwAJvM5/MOFXg6JJ8uNflyf/93yjOppWIq6ioqKi4CWzjTklzJje4MVoHmxRQkxWFeh8qjEwYZLtGoedVJa7iW8c62P+fvfd6byTLsv3WMeHgQdAn01RVd9fM9L3SSJ8e9O/rTQ+j7+pqpl11uUx6Et6FO0YPOwJkVtKAJOiyzq8/VlZXkoggEECcdfbea8UZ1HCOvD9F1p1Atip3svkvN4h44MHfbCDYaSMfTJFjcv0PFdUX2YgQbDXJ0EQs70ppS8fcXCHvT5F86iIfzZ2Ge0aYL8FDD6IaQtRCeGt1+JtNyhBs1yBbVdpAK0QcDzwwyel1Z3jQRuNLYfqXT3fbAHlGnIhzOByOF0zp/MbkPSy7L2Gthcly6HlK7UqvkWLWQlR8yLUaiVsn4hwOwBRiqDtB/OsZog8b9zM4CcjgJHgzQ/zp/Obv9WjBLxsVasGsBnde/FptYDIScelBH3r2ursEXjuiFsLfaiF8u47owya8zSa181cDEnieBPMlmWyV1Tfu8jqfCyfiHA6H4wXDBAPzCzv9B90oLWxO7VGvVcQxX4D7tEssayF44Ll2SoejgGbjxoh/PYNsVRBdnitb8n3CfEnVl60mZC0EE5ys/q+YUeORf1GZqQb0frwLiwriDGowRT6YvrrMyq8FHvmUFbi3jvDDJqJvtxB9uwWvUwePfHDPyYWXiHtVHA6H4yXD+cUA+UOwgMkVdJLDvIJe/y9ggKyGNJfRrBRzGE7AORwlVhuqxP1yhuj9xr0eg0sB2Yzgd+oQjQg88mHS/MqNH1kvXClbVTK3uOv5Kk1tlAc9qNGcBJwzNHkWvI0mom+2EH23jcp32/DW6xCNCnWBvJLWwt8jTsQ5HA7HC4YxFO0qDxdxVhtYpV5nLAdjEI0KwjdrkM0K+AMy8xyOr5HSvAiMFVlrGW12SLF0EZ/MTQJyqWxWIRsVqPEc+goRJ+oRgjdrkK3qvRb6VhvkvSmS/R7UcP5qI09eMyzwwEMP4V4H1X97i+i7LYTvNyCiuwW+O54Hdwd0OByOF8wieHQFCxxW/O9VDjAwBn+tRrvEnbozNHE4fou1sKmCmiY0Y3Y8hJ4k9zIK4Z6A16kj2G1DVK9e0MtGUYlr31PEFe2fycdzqNH87ifpeDD+RgPVf3uLyr/uofKnHfibLdc6+Ypwr5TD4XC8ZCzlKNmHthmVFb3XOITOGS0q1+uIvtmEt1ZzVTiH4wqs0jDzlCpchz2AM8hmBUu/6YtvY56E16mRS2V/CmD0+fcwRq6UO21qb77Dpkq5MWVShbw3QXLQh5rES/+8YwVwBsYZ/M0Wqv+6h8ofdxC+3QAP7zjX+MpY3EeLzhQYQx0q2gDawBoDM09fTVuvE3EOh8PxktEGVtHN5qHW20xwcN+7kw34s8MYRORDNiqUX7XbhqiFztDE4bgOY5H3J4h/PIGoBgj3OmB3fMszT8BbqyHYaSH+ePb530kB5gnIegivU6NK3V0q49pAJxny0Qz5gExNdJrf7QQdD4KHPkTFR7C3hsr3b+BvNu+V8fcqMWTypaYx9CSGHsdQ4/nia/73Q5hMPfdZLoUTcQ6Hw/GCscbA5urhrm2MgXkSLPRe1c2acQZRj+BvNelrs+GqcA7HDVhrkfeniH8+RbjXoY2g4j2/bI4XL0Scv92CqIa/+TsJXvEh6hHZz0f+3c7PGOhpirxfiLixq8I9KYxBVCjEO3jTQfTNFlXg7rAxdlHRKrL+DEDlLVxfxSpNusrjsC8fy2qL4sEun/Bv/rUIFC8iZ5bJpyuPYbWBiTPoWYK8O0F+PkZ2PkJ+PkJ2Rl96PH8VQd+AE3EOh8PxorG5hp5nsJl+UIsHK/LVvFYVeXe8wjN8RBhZnodv11H/728R7LRdBc7huA1rocYxrAWy/hRqlkAiBPPl8l2VglPYc7sKWaU4D6M0YCxEPYS/2YSsR/fKaTSZRnY+QvKpC+3aKJ8ezuBtNlH9fveiAnfXz1VLrbs2U9DzFGaewuQKJtOLFsVS4JV4nTr8rTbYVVEUxkLHGVRvAjX9/JpgxTkzzoEin46H3iJsfCkBaovq23CG2d8PEf98QhW4SQwzS6DnKfQshZ4nMKl6NSY7TsQ5HA7Hi8XC5Hpxg3xQOyVn4JEPr1UBD++2c/5clDfr6F0H9X//Bl67drH76nA4rsYCeppAz2g2Tk8ScCkgPIFlVRwTHLIWwrRrEEUmo7UW1mpypdxuQTSiO83YLqohmUJ2Nkay33VVuGeAcQZ/o4HKv+zB22yCCbF0JYuEWTF7meTQswSqP0U+nELPs+JepWFzRd97SQxF321DtmpgAUkPtijFAdZYmDhFetRDdjL88pwFpzbeQriJegRZDyHKjQQpFlW5y/eHxe9l6F6aD2eY/tdHjP/vH2DiFCZ53W28TsQ5HA7HC8akOdQ4hknyB5mbsMLgwN9uQf56vsIzfByYLxG+WUP4nsJnvXYNPPq6h+4djpViLdSA2irthw2EkX9nF0kmOH1ubDaQ9SbQ4xiyHsLfaUPWo3tUcCxMliM/HyM96LlK3FMjOJgvIVs1BLtrkI3KUiLcKk3B7KM5srMh8u4EajiFGsUkhuKMxJvSF0YhwGeVOBZ4iL7bppnmzyq4VLHTsxTxz6eY/+PwyxPgDIxxqhoKAR5IcN+DqAYQtRCyWYVcq8Fbq1PGXeRTdiG7+HnuS3jtGmp/fgcmOOIfT5D8ekbn/EraJ3+LE3EOh8PxUrGXRVz2MMcswSEbEfytFt1EXzjclwjfdlD/X94j+rABuVZ97lNyOF4d+WCG+JcziFpI7ch3pQz/3mxAxxn0JIGoRQh2WlQFuWtR3JIrZXY+RnLQh3GGJk9KaW7ltaoIdtfI5GqJF9EqDT1NkB31Mf2vT5j/8wjZ6RB5f1LMwV35U5/9P3+zBZ1kkNaC2UsV3GKOzswSxD+fYvI/flniF6E/yKAlQLBHs32VP+xQq6UviypcOX/HyIynVUX1z+8g1+qwyiA7GcIkqRNxDofD4Vg9Js4ouHf+MBHHOIOohvA7tFPprdcX7S8vCR568DebCPfWUP3XPUTfblGYsGuhdDjujBrOEP96Bn+3TRUHY5cygigX9qWxkLfeQNadUGRBPUSw1YL8oqJyMzZTUOMY2emINqbS/OGGTY7lERyyHsHbaEI0IqrK3vL66TiDHs+RngwQ/3SK9NM50uMBsu4IepYAevl7kjVk4w9tiuNeEliCU+UMWO4+V3Z3ZgraWmQnQ9hcQQ1nSA57CPc6CN6uw+s0wCPvIvtOcIhKAH+d8vEYZ4h/OqFqdXl+rwgn4hwOh+MFo+MM6E+h5+nDsuIKRzLGGfz1OvzNJrLzMUz8wArfKmGAiHxE7zdQ/fNbVP9lF9E3W/cyT3A4HEA+nMFkCpU/7cDmhTnSHTZEWFHB9zYa4J+69DlSj+Bvt+7saGhyjbw/RXY6hBrPYV+JjfvXQtlSH+y0qBV2idZaE2fIzoaY/+MQk//4CfHl9sO73jesLbLZ7OdFOoYL58o7ftZT+6ZGlubIexQcL6oBoj/uovl/fg/me/AkB0oRV86GewLVf3sLb70Bqw2Sgx6wChfoJ8aJOIfD4XjB2FxDI4WeJFCjORgns4+72uyz0orZEwh211D/9w+Y/e0QehqTo9hz3bwYA5NkouDvtBHudVD54zaib7bgrdXBvVeUaedwvDBsrsngZDBDWhhGyEZERhBLULpU+p0avFaVzCQqAbWsLfkYJSbNkZ2OkBz0oKfJnX8XxwNhDDwKIFu1hbnVdRXZ0pwkOx1g9tcDzP9xiOx89LBNP2spMseYz8bwWPkPzi6+7uIOacvH1tDGwiqNdL+LSehDTxNU/rQLf6dNTpalQ6ukqiQARH/YgZ4mSPa7SA+6r8aZEnAizuFwOF40NtewykBNYqj+DDzwwHwJds+oNCY4wrcdiEoAM08R/3oGmBRmBWHi9zqfcuB8vYH6//oetX/bg7/dhtep3XmR6HA4Pqc0mlCDGdLDPrgvqSK/7HuLc8iyDbtdhWxVIKoBtafdsWpi0hzp6RDJfg/KibgnhxVVKNmsUBX1BqxS0NME6dEA0//8iPinkwfPZVsLCtr+TfTAxQkCjHMwzmGtud+xtIExFunJEGo0h+pPwQIJXg3gtaqAX8gexsAjqtJV/rADHkgAFulRz4k4h8PhcKwQa6HGcySHPbBAQtaW30n/AkazcUwIVP6wAzVNkB70kR4PoOOssIZe7en/9vjgDCLyIRsRZLsKv1NHsNdB9U+7CN50IOsRxCuJQXA4XjzW0qzQx3PKeNtugWM5p9eLhX8VXqcGf70BUQmogr5kK6U1BlYZqgh2x9TG/cJmcX8XsOK1bFVvFXF6miA56CLZ7yLvTWj+7aFt95faKS0umWKW85egewMT/CL4+77HyXIopZGeDDD/xyGY4Kj8cZcqkIxR2yZjgAd4nRrAGdLDPoJP51CjOfRkBb/vE+BEnMPhcLwC1ChG8qkL2agg2F1behF2FdyXYFKg8qcdiEaEyf/8lVzi+lMo/cjD3ZyBewKyXUX0fgPRN5uIvtlEsNOGbNAu/11t0B0Ox83koznmv57B22zQRs2yFCJOGAtvrQ5/swFRDZbOhgMAqy1MmkNPEmS9KbLu5NW6Ab5qWLF51qyAXxW4fQk1jpH8fIp0v0sCbhXVKWvJCMVcV4kjAccEf/hsmgWgDYV7/+0ANteQzSr8LcrFu2yqIuqUnRrudZC930Sy34WZp7DKiTiHw+FwrAA1niP+2IW/1YJJMtjQo8yfO7o2MsYAwQBu4bWr4L4kAccYstMR5f9MYug5BaHaTMFqfffqXDGozgNJswiBBx5IyvQpjBLC3TUEu23KK2pVwT2xlIBT4xhqEhd22YIeO7x7BpbD8XtBzxKyhO9NoKcJtVR6YqnZWnofe/A3GzDJFrz23eI+bKbI0ORsBD2OYV2swLPAGKOg7FoI5l+z/C/ElZlRK2V2NlxdILYBZchd17pftFNCckCtxszKpDny3gRpJUB23Ee+1YJoVaibBfScME/ACg5/u4XK929gUkXzo+rlm5w4EedwOByvADWaw+Ya0fsN6FkKUQ3BS0F2T3jggUmB6vdvEOyuITseIDnsIz3qIz0cIO9NoMZzmMRef+O9BubRws9r1+hrrQqvXYO/1aSqW6tCv0PkQwQemCeWnrHJexPMfzmDCD2yzO7U4MnlBKDD8XvEzDPkuUbemyIfzSFqES3ml3zLcMnhbzbBQ//OIk4nGbKTIdKjAVV1HM8DA5gvISrhheX+Neg4Q34+gupNV+YiahfGJtfcSBijjclic3IldTBjKKR8OEN60Ie3VkfINxci7uLYgLfRRIVzZGcjsL9y2FfQ8etEnMPhcLwCTJrDKo3sfIT0oAcmOPz1xr2FC2MMkAJMCnieIFFVCSAaEYmt9QbywZTynOIMJlOwygC2uAlbALAXszGcUfWtcBcTob8YoveaFchWFV6rCm+9Dn+jAV7xwYRY2lLaWgsTZ9DzDPHHc8z+sg/ZrMDfalL+UcuFgTsc12G1gdUG+XCG9HhADpOBBG5xfy0r/ZZziFp04fC31EHpDxNnSE+GSI/60G4W7vlgDEwKsMC7dqbaagOrNMw8pY6MOF2d7X4h4q5rp2QMYJKT6c6qYmWKtko9S5Ee9el+1Klf+a2iFoJ5At56HbJRgdIGJlUvejbOiTiHw+F4DRgLaw2y8zGmfz1Y5DXdNqC+FMWgd+la5m80YP64DZMq2EzBJDm1V6YZWU8rTUPn1tKuqeRgUoJ5gloiyxbHwAf3BZgnwT1J/933wENJ4vMu92lrySb9eIDZD0eY/OcnBFtNWKUhGxUSmA6H40b0OEby8RyyGsJbq5FJyTIwgBdtZ3fN8jJJXlTi+tAzJ+KeE8ZJJF33GpqcBJyep9RKr+7pEnkV1gJlO+WVJ1eITE/eOULn1kOnObIT2ryI/rB95fdwj+5LslmFv9Es8vCmL3o2zok4h8PheC0UQmb+0+mFy5xHgukhrYRlhpwIPYjQA5qV4nA0hG4yRdW4JIdRdGMvXcbKnVMmCwFXirXy3+84s/fFr2zIFMHEGZL9LuY/HCP++QxZkXklKgGCN2s0t+dwOG5ETWIkn3rwOw1E325RVZ1dnxdWUs7SsjuY4lqtYXINNY6RdSfI+1Oav3U8DwxgghWVrqvvF1Zp+qwvOj9WWYWyhTul1YbuLV+cH50b9yX0qipxBUZpqOEM2fkIepLAZGphokKHLq9vTmH2m03oGWWzAi93g9CJOIfD4XhF6EmMRGl4rQqCnTaYFPA36hDRkjvqd4Uz2qHkDDz0ilbK8ov+/rJlM7vcVrkKDDmMZadDzP56gMn/+yvy/hRW06xD1htDTeLVtfw4HF8xepogPeghe7NGwc3aAHetii+JyRTy4WwxW6vjzL1PnxWaOePeDW3sxsDkivJJV91GaCx1cujrjE0YuBRkuLPq+WZjYDLqKFHTBHqa0Dx29GWUjagG8DabyHpjcspc7ZmsFCfiHA6H4xVhkhwmyZEc9uH9eAImaOdy0SazwpvfYndeMjD5tKYh1tA8gp4lSPZ7iH86xfzHE8S/nC1MVkySIR/MoCcxLTqMuVN+lcPxe4OyIDUJq+EMeq1G86l89ctBk+TIuxOqfkwT2NxVy58buk9cv8lmjV0ExK9avVhj6HP6mgof4+QUSfezFX+GW8AqA5Nk0LMEunA3xhUijkcBvE4doha9eLMsJ+IcDofjFZJ3J5j+z4+wuaYZM84gGhGE+DpCsq3SyM/HSA57mP7XPqZ/OUDen3zmkmkyBTuaQ00S6CSHzQ2YxxfhsQ6H4zdYS61l4xjJUR+iFsLfad/qVngf9DxFetgnV8o4W/njO+4IA3028hs+I23RRm8tVq7itIXN1fVtmozR/LRP8TmPgdUWehojH0xpnrwYHbiMCD3IVpXmRVctJleME3EOh8PxClHjGHqWkJtWuwomOAKzBjTJgIBd4z72Ulm07mgDk+ZQ4xjxx3PMfzzG7O+HmP9w9OXP5Bo219CzFHqWwKQ5uPCXtk13OH53WADWQk1ipAd9yGYVcq0G1MLVHaJotdbzFOnxAOnJkFo3HS8fBjAaklz5Q1Ml7vpWTcYZuC/BQ68I5H4EjIGZZ9CTGOYal0oWeJCNCkTFB3vhNxMn4hwOh+M1Yi2sBrKzEcb/z8/I+1NUv3+D8N06/M0GZOPLHcYXjzFQswTJxy7iX88R/3SC+Ndz5L3JzT+WZMj7U4ow8MW19tkOh4NQ0wTxfhfeWg3RN5urfXBjYbWGnibITkfIz8erC4x23B8LwBhYpShehn/5OVm2NDK5+o4Gq00RVaMpZuC3CE5zalWy+n8ULLWLmjS/dj6TexKiGoD5HvCyNZwTcQ6Hw/FqsRZ5fwY1mkONY5pnMAbgbOEYyaRYyn3uuSjP2eYaJsmQnY8x/+EY078eIPl4jvR4cOtjmCRH3p9CrTdcXpzDsQR6liA9HCDdbpOdvNIrMySyWsPEOdR4jqw7Rj6YOkOTF0HhDpnr6y38OS9EnFh9Nc4UIi7XV3ZqMk4ijlcD8EcScbYUcZkiU58rYJ4AL3MUX+h9s8SJOIfD4XjNWAtrADWcYfb3Q7LzPh8jer+BcK8Df7NBN+QXWp0yWQ41miM7HyPd7yHZ7yHZ7yI9HkBN4uUeI87IqGE0p8Wow+G4EZvpxWxQ3psg79QhqwEFQT8QPUuRnY6QnY4KR0q78vEqxz2wRZh3rmCluNKQlAkO7nu0AbjySpwtsueuaacUHCIKikrcI8kTS/PWNlfX5tXRcyAf5TlYNU7EORwOx2vHWKhxTALubIS8O4YazABrwQMJHgU0Z8DLofanr8wtbtpF9lyZQafHMbKTIeY/n2L6n58Q/3wGNYnvNEOjkxx5bwo1diLO4VgGqzR0mZ3VncDfmC0yJx+KnqVIjwZIT0f0Pl61Vb3j3pTZfSy4XsCwQFI744pNPaw2sKmCzdT17ZSVAKIePZ6IQ9H5oTTF5VzBZRH3GNEbq8SJOIfD4fiKMKlCdj6G1WReMP/nCbyNOvz1BryNBrxOHTwoQr2fklK0TVPk4xlUf4a8P0F2PkF2OkR6Vuzc38OK3CSXKnHOxtzhWBo9S5Hu9+A1KpD1EKhHK3nM5LCH7GToZuFeEtYWbes5eHi1izETvLg/+OABVeQobuDhQtzmCmoaU3X2iioYlxyyEcFr18g58lEo8kwFv34jk1GeHrUWv2wV50Scw/GqsAt3set2ka7+MVpALwKanwBbnqe1YOZu52vt055rcdC7P68oZroWN7jn33G2mULenSDvTRD/dAJRDRB92ED4YRPV73fJSrwBcL/4+H+Ke5QtWmmUhhrPkR70Ef96hvinUyRHfeTdCfQsvffDX26nNPn1O6xfntcj2Gg/FFv84x7X4mcPY+1L+81WxCqemxWezitHz1IkBz147SrCd+uLz7L7VOrLn6V5uz6y0yFM6kTci6FoJTRJBquudiNlUkBIAR564EVFzloL6NWIOD1NLkLmrzg2ibgqiTiGR/l4ZpwvOlKuhDMwJvAabI6diHM4XglqOMPk//uE9HQE2ajcaafKamofmP90Ch3ff7F8F7KTAQb/118x++sBBWbeoTVDDWfIBzOkh33YXD3iWV7MZM3+dgiTa8jGHXeiLZAe9pAc9JH3p9f22T85Rd6PSRXSszFMrpH3p5j/8wSiHkE2IoiKD1EJqIWlGOSm3VdOO5WCbnaLf790U6W8NkvirLi+bK5hCgtpk+QwaQ49z2DmKfQ8hZ6lUOP54vXN+9QCabKHvcYm11CTBLN/HMEau/RraDOF2d8PYZL8RbRh6mmC+Ncz6HmK5OM5xAOqIsmv51CDGYmdr0C0mDRHejyg/MDeBOP/8cu9Hys7HSI96MMk6ndvuKHjDNnZCOnJEHoak+mF4IC4h4jLFJkMDej9rabJi3hfOQhrLUycQY3mELWbP1uYLyHrFYhaCDuar+R9YjIFPYnJSOeqx+MMzJfglYDuT40KCc50hWuA4hgi8q+P4Sk3iV7Bbo8TcQ7HKyEfTKFGc+AvRYbLXe6x5WeRNk+2aEmPh8jOxxfuTnc532LhaY25und+hdhUQWUK00mM2T+P71eZ0vYihPolffAbC5PmZDJwPgZjJ2CCQdQrkI0I/nod38L2XAAAIABJREFUXqcOb70Bb70O2Ygg6xHtwvqS5gJ8SQGsIPvpUhxarRdOZybXMHEKM8+gCrGmRzHUeI68N0HWmyLvT6D6UwrlLiymV1VxtZmCzhVm/zjC/MeT5V/DYtD/Opeyp0ZPE8SFgLtxp3ipB6PX6GsQcAA5kKbHA2QnQ0z/sv+weR1DGw+P/dnyGjBxhixTyNYbUJMEJlO0kXMPHySTKZrNLTZp9CRZ/Qk77o+10HEGPZrBrl+dkVbCPQnRiCCqIfQspTm2hx6+EHFmdo2IK8O+I5/uUc0qlLXQKxRxjDFw3wOPgutjDCwu7k8vHCfiHI7XwmLB+dwnsiRF//2rwOJJBe6TU7TDWABWAWAxbK5g0hz5cA5xOoSohhBRMQfhCaq+SQ4mRLEzzy/clsudyjIeQJHjmcnJutmkCibJYOKsCOIuKnGllfljLJ6/ltdw0Xr8yn+Px8BYWNjX8xn4KqDFallZf0iLsZ4mNAt3NnZtlC8RQ5W4fDiHf8usIg89eGs1yFYVajBdyaeRyTXdC+K0MBYxJNyKG0v5J/c9+FtNhO83kBgDPV7OpXgZmOQQ9bCYu7t6LtBqQx0lSr+sTdkrcCLO4XA4fmeYNKfWlllCN9HfulZ+Vj29qPouNByu+JfFDCQu5gvLWcxLbpQOh+OF8sAFq5rESPZ7SM+GK6ncOFbLop1yOLvVcIYHPry1OmSzsjKnSJtraEMzcTYvNvP4l11FzBfwt1qIPmxC9SdID3orOT4AQHDIegTZqUHcIOJskSP30u9YTsQ5Xgecg3kemOeBBwG474P5Pv03KemLCzBR2OIyfmkxemlBueh1NkBpM6sVtYYpBZvn9JVlMMWfVqkXvxuzgHMKzAwj8DAEC3x6rjwPTBbZL0LQgp1zsGKBvlh4G1O0yamL5yPLYNIUJklgkoRatPTr3wpnvk/XUqUKUauChyG451OeGmNFS4WBVQomK37/6Qx6PofNM9j8jjvNjNHxajWISgU8isCDAExKQIhLhjX0/Jo0pa/ZHHo+g81y2Hx52/0b+Y05ziqv7sX7NArp9/OL96tH71PworK3eI9i0aIJa2h3tnxf5jlMlsNm6WfX4EXFwOFwPAQeeBD1CF6nDlGELN818Lu8t+pJjPSwj/x88uA5V8cjYCz0LEE+mN4a4cIjn9yM1+pg/oqkgrWwmuYw1XAGNY4hauEXj889CW+9gfBdhuSgC/GpC5PmD9oYYJKDV0P46w3IVpWy6Pyr2ymtLma682uiEF4QTsQ5XgUkTEKIWg2y2YRoNiFqNYhqlRbDYXQh7KQAE/LCgchaWEtzL/aySMlymDSFXSwOY+jZHGY2g5pOoCcTmNkMejajk3gFi0YmBJjnQbSa8NprEI0GZKMBXq1CVCpgQUAL60L8orTQLQWcymHSQrTFMT0X4xH0aIS834caDOg5+wpEHA8CyGYL3vY2gt1dyHYbol4D94NCVFmYPIeJY+jxGGrQR3Z8gvzsFGoyuZuIYwwQArxahb+9DX9zC3J9HbLVhIgiMN9fzACSaE6gRyOo4QjZ6Snys1PoyRRa5S/7OiyEqqhWIdptyFaLrsF6A7xSIWFXbioISWIOrKjSmUK4KZg0gYkT6NkMejqFHo/pGhz0aXPhK9lIcDieGx768Dca8DeblM/ly4tK/LIUVXY1JlfKvDt2lbgXiLUWepYi701uNTgjEdeEt1a7cDJezUlQJEx/Am/UAA8k8JvHZ56A12kAAIKf2kjaNeTDGfRDRJwn4bVr8LfbJOIqwbXXuVWGDFUy107pcNyNonrG/QC8EoFHFYhKBF6pkGCrViGqNYhqUcmIoks7/R54UdVYCLhCxKHs+TfFTn9ReTM5VTdslsNkKWySQicJzHxOX/GcxEwp9C5VA0ySAM/pRMgYVSB9nxbNl5+fRgOyXi/+f5UqQGFIi2ffL+acxOL5Xjw/WhfPSSFwkwRyvgY9ncGbjKHHE+jZlBbW0xnMbAqTZbDZiipEt8FpRkvU6/A2NsCjym++gRYTJkuRn51B9ftfPISo1SBq9PPe1ha8Tgfe+jpErQZeqdBzxHkxP6Zg0xS6Xoeo1yFqdch2G3n3HHm3Bz0loX/j+UoJUW9AdtbgrW/A39yAXOvQZkSttqhSLSIZVA6b5dC1OkSTRJDX6SDvdemYkzH0dPq8115JsbnCw4iuwVqVnt/iueJ1qjqKSvViA2FROS9tntnFe/RSRdxkGb33EhJzZjaDPx5BjceLzZXyyyrlRJ3DcQ9ExUewu4Zgtw1RCeh9eUdMnEFPqcKjxvMiB+xlL35/lxgyNmHDGVn9JzltessvM9O4LyGbFYgmCR7my5XNM5s4Q3Y+hreoiv3mGxij49crCD9sUpvur6dIPhqYTN1tg4AzME9AtquIvqGYHa9TLzYQrzm/hCqFOk5fvLmJE3GOl0UhTES9Bm9zC/7mJrytTchWu6i4BUU74EVbICsWguWfF/M8l/4sh2eFALs0r8MXLVxUCShFntUaMBo2pwWlGo2ghkPkgz5Uvw/V6yEvFpvPtlPDOVgQQDab8Hd3qZq01oFcWwOXEszz6Pe9JGoZYxcL58sf2peeHyElbBhCVKuwxsDT+tLiOoMaDqGGQ6QHh8gODqDGY2ilnkRUMCHAggD+7i5q//6/wd/a+uzvraUWWT0aYfIf/3GliJPtNoK37xC8fYtgb4+E2xXClllLz6GU4GEI2WjA39qCns2Q7u8jPdhHur+P7AYRx6QEDyP4Ozuo/Mv38La3IWt1EtSfbTgwMHvxO8Lz6ZitFvytLdgsQ3p4WBz3gNpan0o43wATArLRIHG6uwt/Z6cQcTUwIS+uv/K9evn6Az7/01qwQvTC98ErFaBev3g/ag2T04ZBfn6O/Owc6dEh0sMjmHh+YczgcDiWRlRDhHsk4nh09YzQbahpiux4WFR4Mljl3osvkqIKpgCy+p8lJNDkl6878wRELSSr/1oEHvowcUrt/g9Exxmy0yH8jQaCvc7V38QAHnmI/rBNLZeSQxWOx3epyDHBISoB/I0mKt+/QfXP7yDbtRt/xiQZmblMkxfjXHwdTsQ5XgSsqBTRArABb20N3vo65Po6vDVqC+S+Twu8ex/k852m2xpGFvbnWtMcU6MBUa9D1etIrIUaDu8+F7UCmOeDVysQdaoKeWsd+Ftb8DY3IIvKzWXHp+UfuPh+zr98boobsrWWqqKNBrgfQFQqyPs9qF4PejK5qIo81g28EOu8UoG3vg5/Z+fz0yyqiXkhupjv0/kYU7TzRfB3dxF+8w28LWpp5N41eXuXnr9SWAAgcWEtmKSdSRPHi6/PfpbzQoRtI3j/HsHeWxLY113Hl5/0oi128TyWoenFz5k0gRqN6JhPWYFijESZ79P112xSRXNjE97mJvyNDbAwJJF63+sPoN//N39trYUoZllplpFeT1GrIe/3oYdD6PmcKuSuKudw3AxnFOzciOBvNuGvN8CD5bNHL6MnMZKDLrLzMUz6imbIf49oA5vlUOMY+fkY6NSLYO3frI+KDXJRC+FtNuB168jPDPQKHKdNnCI/GyHvTiij09gLUy1cCpoXHLJVBfMEKuM5YCyy8zHy8xF0TO7H5GtwyVm1yDXlvgQPPYhGBf56HeG7DYQfNuGtN26d8TNzqhSqSfxycl+vwYk4x4tAVKu0CNzdRfBmj2ZpyhmuctF7jzaPB1NWp2o1EpnVCmS7DT2dIvn48elvVoyBVyIEO7vw3+zC331DbYVhAB5Qu+Sd5xnuevyoAu75EJUq/N1d5OdnyE5PqUr06RPMfP58M3Pl7JnnkalLEMAAsHlOgmq7EFTv3tH1Je4ehsSkhOx0wMNwYfySnZ5eGG4Ai3PwNrdQ+fOf4e/sQLZa4L5/v+uYMchmk45pDUyegR0dITs5edrnmnMw34dstRB+8w2Ct28h22v0uwXBoqL5aDCqWopGEzyMINtrCN69Q3p0hPTTR2QnJ8i73a9iZtPheEyYJyAqAbxWpTB7qIDf04VQjeZIPnaRn49duPdrwAJ6PEd62Kf5s7UacM1tSVQCBLtrUIMpVe+mD8/+M0XAfN4dwyYZdfFclYvJGLgnweoRqv/6FsHuGtKDHpIDutaysxH0PCUxVwg55gmI0IdsVOCt1+HvtBG+24C/3YLXqtFGxS33YD1LSCiO57D6ZW9IOBHneB6KljVRqYLXqvC3tuDv7MLf3UGwswterV7MzTzbKV60ejHfB4qqDkuSxxVKV7EwdqFZruDdOwS7u1RNarXuV3m7C+UOGch9EJ4HHkX033yfZhO5AMCgel2qEmXFh/MTCt2FI6kQ4AG1hIIxGMYhOx2E33wDf3sbstm8e1X30i6hiCJwz4O/vb1wNFX93qIKKapVatvc3UXwZheyvVaYedxR4Fx6TXlR4fI3N2FTck1VoxF0nj9+K2s521et0gzh1jbCDx/gv3lD1fPKb2cTV89n78cgAIpqnG236bNC8IWIVMMhbSY8Q6Xc4XgNiMiHv96At96gUOdr7NZvwha5jGo0R3I0QD6YORH3CrDWQo3nSA66kGtV6vK45nt5JUCw215Y/a/iE9XkGpjEyAdTZN0JXYO1CCz8vBJMIx4M4Axepw7ZqlIQeCNC3h3D32qRiEsKEWdIxPHAIxHXqVOVeYfMTJjg164prbWLnFE1niM9HUKNY9dO6XBcSWFQ4W1vIfzwDbUDbmyQO2AU0QL7qYXSEujpFNnJCdRo9HQtW8VzJdtthN9+i+DNHryNDap+hOGzP0+iWiX7eD+AXFtD8vEjkp9+XDhZPkdrTelmyuuNQoCk8De3EH7zLUSttpqqLueQ6+tgvg81GiLZ/wSgqPq126j86U8I3r6FqNVJ+K5oQ0I0mgg+COj5HNnR0YVYecTnmeZU6/C3thD94Q8I9t5Se3Gt9rAW54dSzNnJtTXa5KjXIWt1pIcHSPf3SeA6HI4vELUQ4bsOgp3WvdsoTa5h0hz5aE7tZ+M5zcM5XjbWQo1mSPe7CN+t33jvEJGPYGcNeW8KXgnI0fqh5ibGwOQWajRHut+FrEcI3q5TW+d1MJpv89q14pza1IpZCC9qpwS1CQtOURm+Ry2VkV/E2ty8VjK5ohy9wQzZyRBqNKfHfsE4Eed4WsoKXL0O2WggePsW0bffQnY6NMN03XzSffjtB9N9xc6lxylFnB6NnqZlizGa+2k04L95g/DDBwS7byDqZI6xLPbSbBWMWVi6L/5baeZx2fRkyeoeL1wHuecv5vFMmgBCQPX7NLf11H3ljC3MSIzvw6p8MWNZ/o4LW/vC0Kb8uYUJzG2ii7GF86dc60C22zCzGUySwFtbQ7D3Ft4mzYiVFbjFnGXhxLi4tspZs/KYNzzvPKT8Na/TgWy1YOZz6On0capOCzfQBvztLYRv3yF4/wH+zs6FYcmSLIxHSifUy1lvl6+5y0ZFt1BWXskBs0JGMpJaik2ckFNpElOF1OFwLJC1COFeB8FWC+KmxfMNmDhFPpgtXClvC5B2vBCshZ4kSE8GFPwd54WpnPjins+koOpXxQf3qePBwjxMyNnyHGIkn84h6iFkuwrZqly57rjowqD2TlEJ7n/sG9ATaqPMzsdQozlM8vzmYbfhRJzjSWFCgEmJ4M0eoj98Rxbvm5skSB5zlmZFmOkU2fEx1FOIuEJIeGtrCP/4RwR7e/C3tu9f/bD2IsxcqYu8rdIV0JMUCL7IkLt95+oyzPMgGKMFvich63XMf/gBqtul1sonnt3iYQjRbEC2WzTHtta+EKkAxUxcCtUG6PrkPjlDLlU5K8SGbDYRvNkj+//JFLKzDtnpQFRrnwud0kY/z2HTlIRXOXdZzIAuc0wAEDXKnDNpSjEPjyDimOdBVKvwd7ZR+f57MmdptS4C4++CMRQdoHJyfdXqolWlEIuscFXl96xc8iiCt7VFawRjwHwf2fER9Gh058dyOL5KGGiutB4ieLMGf7MJFtxvKZgP54h/PUPenbgK3GvCAnqewiqNvDdB3p/CQw2iFlL74uVvzTX0LIGek6ChOJx8JVEDahoj/ngGXg0Qvt+Ev9UCk+KLc3gSLJCdjzD/6z6yk8GLr8CVOBHneDo4B69WIet1BG/3EP3hj1RRqlSWnn1bVDIWod16YT9OFRVdfA8AW85j/cbOnDMwdqnaUi4gOV9Yol+uBFhgkSunpxOofp8CwB+5usR9H7xahbddzB/t7FLlx795dsFefn7KBXO5eE7Jot1mGUye0+9lLfWKex45XwY+uOeD+aWg8y6yvUpBcoW4Kyszst1ezEipyQRQimaULrs3PjKMc/BqBd5ah0xfwgiy2aQKXJZdZJD91llSCoiossiMK7P16Fe+enfQck5V5d1dqHENujah3Ll6HbwQZVYpOmYZol582TwDwEgs1et03CgED8Jrq1Fl9YlXKvA2N6EmE+Td7oqfwIsKl7e5iWBvD8G79xTpcEOV8rNKY57TNVZsFpg0hU3TIiYgh1EK0AqwRazCZ4Y0VG1Ecc2VOZC3VYjLqjCAxTymmc/ouX6iGAyH4yXDpAAPPchWFf5Gk+aM7mBocjk3Sw1mSH49R96bOEfYV4bNFHSmkPenyE4GYJKDRz6ssUU+qoLJcqjhDNnZCNnpECbNqa0RDKto3jdJjuxsBNmoID3u09xbs0Jtm/jynvsYWGthi7bg7HiA2Q9HyM6ecFzmgTgR53gaivw3f2cX0bffwt+jlkDm+3drc7TFB0yWFVWPySLw18RJ0TqlYXVRZdIGJMPYwnqWSY96pQNydORRSLb5FQor5lGRG3ZpoWriGHoygRpPoOP4SfLhRKuF8MMHhO/fw+usQ1Si5drXrKUw8jhG3utB9XuU5TaZFIvonAZ4jb7oI2fUR14aWPAgAK/WIOt1cgpttSCbTchG49bDM8GBwIe3vo7Kn74HDwIk//wn8ix7siwvJgSZlwhJAiDwwUMyYlGDAbLTE8oa63Ypc62sYhVmO7LVgre1Re6La2ufV/C+OBgDr9XgbW1BNBowcQyv0/msWqqnU+Tn58jOz5CfnUNPxrBZTtcoK2bOajXIVgvB+w8I9vYWoePXwYMAstWCrNdXP5dWumuuryP6458Qvn1Lr/0S1dkyrDvv9aC6XagiJN7EMUya0N+XLayX2lhLccikJFfaKIKsNyjsfH2dntNSzN0CD0OqyKkcejyCSVPo8fhJNxIcjpeIqIXwNxrwNxoQlQDcE3cfNSja4fLBFPEvZ8i641dTuXB8jupPMf/pBMyX8Dp1mExB9SZIT4fIjvrkItmfIO9OkPfGsKla3WttDGwO5P0JZn/ZBwxQ/bc9BNEd14UPOgcLNZgiOx8j+XiO5OP5q3ClLHEizvH4FG5yolJBsLuD6Pvvl67ALXb9jLmoZsQxzGwG1e8hHwygh0Ny6ZtOKacsL3b5i5bBBUWlqGyX41FE4q1Wg2zQYrHMWaNg8WKWiTMK++73occj2DR93F2aoq3MK9wUg903ZC8fXDMDdynDrRS4ZSB3dnSE9PiIAsr7/UUr5Y0wBh4E9Hy02/A2NskRsWj9477/mfj+okLFBRgXkM0WmOcDsIvq5WeC6THhHKJWBw+jRYueVYqE7fk5kp9/Rnp0hPzoiFo9LwlLHkWQrTaC2Yyugyha2Odfhyiu5fL55REJ7jIkXQ0HFMGw/wnp4SEZ41wWtEU7pWy3wTyPrsdaDbhBsHA/oOu1Xr8+6+6ecJ/mG72tLaoCb2/f6BZrizxFqlbT+zA7PER6dATV6yLv92Hmc2pbva0axqkqzKMI3loH3vo6bErxDWW1EkXl7raKnFUK/mBAGy9KfR4D4XD8DpG1EOFeB/5mE7ziU/vaHbFKw2YaeX+K5LAHNYqdiHulqOEUyc+nkPUI3noDepYgO+oj/uUU8U8nyE4Lg487BGwvjQWgDdRwjvjHYwCAbFWpOhx4QJHn9hgVOVovaZgkR3o6RPLzKZL9c2Qng5W0ij4VTsQ5Hh0mBLz1dQS7u/C3dxYhzEvttBSLQz2f085+r4e824UaDmDmMUw8X8w0Lea9LptVXF6wFa2DphA7Jk3BplOo4RC57xcVm7DIg6PoA1lvQNRrJIIGA6jh6NHDH0WtBtleg7+zC299gyqWS1RarFLIT0+RnZ0Vf55CT6cw0+lCPC01x2ctVeum02JRPkN+fgbv5IQCtnd34b95s2ixvA4mJUQUQa51EL5/DwDIDg+hhsOln4t7UxiFLFr/rIUaDpB3u0g+/or00yeo8ZjE/m8W9TbPocZjsLNTiHodAODv7NzYxsqkpAw33weMWbT+6aLVMd3/hOSXX5Cfn0HP519em8YsqkXp4SGY51OWXa12/TE9WVSQK2B+QOHjl81SHoBsNhF+9x3CDx8urr+b3q/G0EbHYEAbB0eHVCUfT2CSon102XbGcsOmqCSbeA41HCLd34e/9xbB3h5ks7kw0bkJUakgePeOHjZOyABGqVfTKuNwrBrZqiL6bgvhm/b9XCktuQrm/Sny3gR6nsHmCivpr3M8OWocA+jCGoPsfAQzT8moZjCDGk6hZ+mjC3SbKeSDKdinLmTjE6zWCN9vIthpFy30j3BMZaD6U2RnI8z+so/ZXz4hOxm+uk0+J+IcjwujeR9/YwPht9/C294Cr9Vu3VkpZ2vKxZzq94tKxj6yoyPkve7dM8jKaoHWdL+5rrWKc8q+qlMmm7e+DjUcQo9GUKPHf5OLWh3Bmzfwd3bgdTrXL+R/U4EzsxmykxMkP/2E9OgQ2dHR/U9Ca6qczOcA+gDnyM/OINtrsFrT7Fa1ClGtLu7dX1TkFjNyLQRv38EaAz0eQ43Hjz6bxBgDpLx4rbSGGgyQ/PIL0k+fkB4dXbuQJ9OXKVSXIfUD+h3qddhW6+rfszjWZUFbVpD1dIL08ADJx49ID/ahJ5OrT9haahE2BtnJCQBA1Ouwe3ufH+fycYWEiCR4WFQKpQdrLGAfIFCKGVHZblOm3ps3FCFxTRtvOX9p0pTeowcHiH/8EfGP/3xYRmDxPtVpCj0aAqenYFIiimPSbcZQTmEhlq+tyEUR/J1dAIyCwM/PaD7OiTjH7w3OwDiD164i+rABf6sF7t/HIKsI9/7URd6bUtCyMzV5tehpAj1PkQ9nSH45hckU9GyJjokVYpWGHsfIjMVMcphckxlZPQLzJV2njK7fex9j4ZVAFTgdZ0iPB0h+OcX8bweY/udHqsC9Lg3nRJzjESkcAmWzSeYIhTX+UmgNk+fFwnAf2fEx8m4PajCAnk0fN0S6mCkrRaQaDmmWrDCleLTjFqLHW1tD8P49vM3NpeZ/bJYhOz1FdnyM9ONHpIcH14uF+2ItiWn0kfzyM0ySICjm9bjv3+gsyv0AstOBn8TIT06pQjOfw2ZPZN9buEGqfh/px49Qg8FSNyiTZch7PfBqBcG7tyT6lnXsLKpJajhCenCA/Oxs4YB5E9YYmOmUHD1nU1itb57Hw0UVkIcBzYKa+wsUXqlANhrwNjchW22ISvXGdqvy/aF6PSS//ILk468Xz+8q3yfF85mfnmKWK5g0A/N9aq+sVq+//oqKLI8ieBubCEZjZKcnUE917TkcLwRRC+E1K/C3WpS1VQlofvmOWGOR9SaY//MYWXf8qlrPHFdgLWAKoxNji8y15xHlJlXIz0aAsbBZjuxkgPDdBoK9DkULVB8QLWAsCdRpjOxkiPSoj+TjGZKPXWSnRQvlK6vCAU7EOR4RxjllnLVaNFe1s3Prz5QVjMvzS/Hf/47k06eLUOPHpqyKZNnqxdANMCHAgxByrU2GGqWV+3WnWZyrSRJkR0eI//kDsmOqNqyc0iwlSWDSDNnZOZgQ8Dc3aU7uBqGxmE/Kc8j1DkS/R1WWJ1pIl86IZTV32ZZDm2VQgz5EFNGspVIX0QvLHLOYTcwOj8g9cslWQj0l8VYeE5ddQS9TPt+FiGNBCPbAmUNRqSxiP2SrtXAZ/eL3u/Q+Vf0+kk+fkPz8M5Jffr73sW/FGDKjOT+nucFWc5GjWL4mV1ZJhYCIIvgbGzCzGTnMDgaPd54OxwtE1iIEu2vwt8iR8q5ZW5fn0/PeBLMfj5H3Jp+5VTpeKYVDo82ft0PB5gp5bwI1miM7HyHZ76Lxv38HHkigUwfzxcKdecF1+5vlZVlcnyanCmPeHWP+wyFmfz8gAXfUf9Tf6bFxIs7xaDAp4W1uIvzwgRZcy1BY4+fFzn66v4/8/Jxmur5ye3DZoIqlt75O7XG3VGBsntPM1dkZsuNjZKen0PPZo5+nzTOYOZAdH2P+t79Rft2bN7ea1PAwhL+zs5jPexKBbAzMfF5UcOd3c8csWwXzHGY6g5pMIKpViCXmE818jrzfpzzBPLvbDt+lFkUznZJJyjViCqDNEhYG4IEPfY9stYsHYhCNBoK3b+FtbNxo5AJjaKNjOER2eID055+hhk8njFSvi/k//oHI2sKF9GaHPeZ5kJ0OvCRBenJMrbaX3TEdjq8VBoAx+FsN1P7bW4RvO/dso7TQ8wxqHBdOhVPKDnMizrFirDEwcYb8fIzpf35E1h3Da5PhiWxWIBpV8IDaLJkUYJcdVjVlsZpcw6Y5dJxRy+gkhhrNKFLhbIS8O4KevH63YifiHI/GYhbuwweIZmupn7FFIHDe7WL+t78i3d8n98DbHBW/AsoFtOysU4vibc6dKoca9JEdHyE7PkJ+evok51kayGTHRzB5BuZJeFtbNzopAgAPA/jb24u2uKc5WQs9myHv9a42FFni522eQ8+m0OPxrRl9JTqOkXe70KPRveIobGl0Mp2Q3X8lwnVbjlTBDSgo/L4irtjdlI0Gwrdv4W1sgvvXv57WGJgkhhoNkR4cIPn5pyfdkc97PejpFKLeQPTtd0AY3izifB9ybQ1Wq4VRi8uNc/wuKOJj/M0man9+e/9ZOGNpdqo/oYDo7sQJOMfjYCxMnJGQ602aqfxLAAAgAElEQVTA/voJolGBbFYQ7K0jfLsOUQvpK/LBQ7+Yl2MwqhBv8xR6kiAfTJGdDpGfj5B3J1DjYjP3K2kDdiLOsXoKMxNerUI0m5CtNvgti6wSPR4jOz6i2aVyAfy1mxCUwcr1OvydbchW60b79IWZxDwmI5PCafGp0fM50OtBdXtQ/T7Qal3EMlwBExKiWiNb/FYLolZbuIo+FrZoA9Xj8cKm/s4YTTmBsxkFhi9z3DSFHg6pJfI+128Zlh0n4NV8EXV4JYKDez4FZd9TxPEooipjq0XRH74PsOsfy2YZsrOzhcvnU1fJyxZZPR4jOzuDZy1kvU4VtqsoZuOYH1DFu9OBGo1gptMnPW+H46nxNxoItpoI365DtirgoQfcwyDCaoPseIDp3w6RnY2dgHM8DdbCagszT6GKuTU9noMHHpmeeJcrcYwycLWGySjA28QZ9CSGnpGBC41TPPcvtTqciHOsHs4pZ6xGC3bZbt24ILyMHo9ptubTJ1p4P8UM3HNTBB2Leh3+9g5VCm4K9baWnB7jGNnxCdJPn8iI5YkxcQyTJMh7XahejyIHfP/6cxeCjDOazYVFvC2F+mNRiDg1Hi9lLHLlQ2gNXWQTmiXP1aQpiYTZ7N4Ch0RcDOQ3V6EZF0UI9u3V2+vgUQTZWYdstijs/paqqsky5KdnSD7tQz/DBsLCvXIyRn5+Trl2UXRzFAfn4L4P2WxQVS7LnIhzfPX4Gw1U/3UP0bt1yEYh4u6BVRrp8QDTv+wjOxut+CwdjhuwgElymFRBTWKwwz6AInqAAezSDqct/2lx4UZZuJ1/LdW3yzgR51g5TEqIZpNmuyoVMH57mKhJ00IQ9JCfn0MPh78PAYdLVZB6HSwIbs3ksnkOPZ1SgPZkQjNmz9FuWnww6skE2fERWOBDNhrXtlWWA8nM9yFbLXgbGyRUZo84x1eY1DzIFKdwyjJ3CHm3WUYZfUkM3Cdjx1pAkTnKrZW8ssok+L1DUUWlCn9zk+bLpLy+ClzMkdkkgRqRK6W5LqrjCTBxDNXrQTYbsHrj2u8rfx8mxKIa7MxNHF8tDBBVajeLPmyi+i9v4G+1wLzr39vXYbWhNsreBOnJCNnxAGr69JuGDgfFRNkvUnS+Pmm2PA+Ygnc4roac41rwNjchKtFSP2OK9jPV71Go92Ty+xFxlQpkZx2iXgf3vNtn4fK8sHXvQs+m9Dw942yPnkyQHh1B9Qew+nYxyaSEbLfhb26C32DYsRKshckz6Pl86Sralw9hyMwlTZdujTR5RnN0cXxvy3+rFWy+pIgr8t3um4rKqxV4W5sXJiHXnlQxIxjHUKMR8kH/3hXOVWDiGHm/Bz2ZLvfaCAFerUI2m+D+A+yqHY6XDGOQzQrCvQ6ibzZR/f4N/K0mmLxHpIA2UOMY6ekI2ekQ2dkYeuZEnMPxEnCVOMfKYUJQ1lR7DTxcUsTN58jOzpD3+9Qa+LXPwZUwBhFVKNS7WgX47dUUU4i4xXP1zLMJOo7Bej3oyRgmy8GLPLXrfg8mJUS9AdnpkD38o2JhcwWTPqBaWYSDLmOEcWG9rxcunPd9fcoQ99vMWFjRjssYv5+GK6/BtTXwWu3mWAuloGcz6PGIKnDPbDhksgx6PIaJ50ttZFAlrhBxgRNxjq8MxiCqAUQ9ROUP26h8v4vw/QZENbgx7/FKio8cmyukxwPMfzhCdj6GVb+Te7PD8QpwIs6xchaL9LX20gslPZshOz2F6vd/NxW4El6JChFXWcr8xeZZUbXsP8ss3BfnkyRQSkFPJrBZBmvMl1kul2BCQNbrMO01Mrx5zHOz5OJpk+T+gsOSZbFVarn5tkJ82TQjEXZfkV3EbdhlglfLStxd2ykLAcijiMK9i42E67BKQY3HUIPhs1bgFudTZDmaOF7qtWGck+FSqwkWOhHn+LpggkG2KgjfrKH257do/B/fUUDyPUK9S0yqkO73MP3LPvLuM8y/OhyOa3EizrFaOCdnykoFola/OWsKWOR2mfkcqtuFHo2fZ77rGeFRBNluk6HETQvossqTK6jJGGo4hH2iwOybsFovjFb0dEKvfaVyvRjgHCwIICoV8DACCwJ6zR+l+kpVtKVmy659CAsYTV+3CbJSeGlFbZT3NTVBUYlbJhKhaKe8jzMl831w3wevRGBhePs8plYwcwrMfhHXnlIUQJ9ln1csr/sdCnMTHlWodZmxZ69kOxwPQnBwT0A2K/DWagjfrSN6v4Ho2014nTpZrxcGEHfBKA09iZEeD5AcDZCdDKGmz79x41gNoh5B1ELYTMFkijpW8uKe5T4SXw1OxDlWR2GwwD0PohKRqclN8zXAosqh53Pk/R7U5Hcm4hiDCEPIVotaC2+rpJQzSRPKLbvvnNfKsRYmSaGGI/BqjXLLbrJ7l7IQchFEpUJOl48h4izIyfAhIg6XBNUtdzeyNi5yDR8iDhZuWub2/LWimgZ+ffXzOngQQDYatIFwS2A2QPMxZk5xCy/hfUqVSrt8xbOMP/F9MOkBXBSLFpcX53iFcAYuBUQjQuXbLUR/2Eblm02EHzYgqiGYuGeLNQCT5UhPhpj/dIr0eIB8OIe9j0mT4+XBGbxOHeHbdajxnL4mMewkhs2t29h6RTgR51gdnNPOfhSB+QHtdN/CYic9nlNL1AvY3X8yygpKEcfAff/mRbQp809SmPRS9eGFsHBkvKW1beFSKQV4GELUarQIf6TWUGuWm2e79ueBC1F1y73Nak1h6GqJqt2tB17umAAWWYN3Xa/xICBDnTCka/E2Eahp1s/M5y9CxJFbmV5Ug621Nz4PF9eeXHxRFfjlvI8cXwm8+IzzJWVaeQJWG/ps0AbWWFhT2p6XNuj4/HODsWLmtbhXSF48pgDzPYjIg6iF8No1RN9sIny/gWCnjWCrufj5u2KNgVUGehwj+XSO+Q9HyM/HsNkLeL87VgLjDP5WE9X/9g56mkBNYuhpDD1NYJKcqnO5gs31okJnF/9fwxTXsOP5cSLOsTKYEBBRUYG7Ka/pEmax8E+ePDD42eEcTEpq7wrDW7O5rDHkkJikDxIlj4X9/9l7r+Y4sjVJ0I8KmQqCAFVVdfdtOTO2azu9+/8fZ832Yc22Z7q7rqhbRVEkQYhUoY/Yh+9EIEFCJMiEDjdLRiQIZEaGyuPHv89dN7B5Tn1662wb42BRBJ6mYHl+Qxvl/KBkzX62K17rSlhLJM5sgMRdG9cfsLEg8CQuXK8f05+DdxZrcRGs71s0Zi1FEQA5VSoF6+xXltU9enwvmOAQaQg1TqF2h5CDCKasKe+qrGHKBk5/QeqM9SXU7Wv46pZA0iMJIAYR5CiB2hpA7QwQ7A6htgdUHpeG4PHlLQxXwRkHW9ZojpcofvmE7D/eQ89v6P7c427AOcIX2xj+H3/nTbuMJ2qGQrGXJcyi6BQ6Wi9g5jnMsgQyInu9Ynf36Elcj42hU5WuCt1dQWdM8K15Wg8YTAjKhVMB5fdcVXpqDFxZktJ1nwbQHq7RZOVfVeubTIQhRJpCT6eb3ZbVLxdr1+stu/jFVp9c/rvWUinlRgn2dbb7ekSOBQF4mpLd/hrEh0kJMRpC7e2BxzHsTZHva0I9271yEuQM2mw9pcB007eA9Ng4mOAQcQi1M0Dyd/sI9saexNUwRQ1beRLn+5Cc8RNOK9+DrfrGlQQPJXgSehIXE4nbJiInR4mv7PjG2kmc3jNtWaP6OEXx6wHKd8eoD2akGPZ4PGDUExe82OqqL5wPwzZFBbOsYBaewM0Lv8yJyLWKXVHB1kQAXWPOkkG/7ox9cuO620ZP4npsDlxQL1QUX01IPM4ocU+pL4Ux8CAgwqsU1hl8O2PIPKQs7mUEQ6vEubWVOAYehOBJslbp7Tdtk/NlSrekWnbv59yDIAbUv5qABevtf5GmiP/uDwj2n3vF8X5MJojRGHI4XF+Fg59EURKo+rjUHpsHExw8VlDbVOoY/2GflDb/aNW3tmzataWUX5RTtuSMCU4PRectDyV4oMBDSf2wm4B1MIsS+c8fsPi33zyB640ungw4Aw8DqqpKAqjt4ddlld4IxRY1zLKA9qpd++hKMxclTFHBFnV//twgehLXY2NgnFMvXBiuTeJc08BkGWy15sD/EYFJSe6M66qWxsBVFT3uJYk7dQpcS4ljjMr5rrEPrrdB7sqMtRtB25v1AL64mJTgcQyurujH9OBhCP7sGdSzZ7ewdTcI5gfFUn6Tq2ePHleCM3AlqaTy2RDR65273qJL4WoNvSxRvj9G/pePKH45gJ7lD+I+1uP70SpyTAlACeCCslzq5bR0vrQK3SzvlnplabISJquIBBoDp/3khVfpXLfuyFyqP9eujZ7E9dgcODm/sSAA1iVxWntDkydWX80Y4B0a1yYwzlLmWXP/+uEA3y/VqjPrOgX6fbDu+XIttGWUt1kKtKLEPYRvJCYlZTlKebWpySMCA7qe1Mty8Xr0eCrQ8wLZHz8g+8/3KN8eQk8z2N7MpMeXYDRhj0BCDmPwUEFO0lOFrtZ+vaH+z6qhHru21657UFmmXpawvry4L728PnoS12NzYBxcSnCl1p/dNhquroicPCUSB1/OFQRrq5awPkTa6Pu5r6wnmes6M7YkLlDr74Nr4LSM8nZJ3J2of98KIcCCa0wkPBYwfHtAeo8ejwhUItegPpgh/+PvyH9+j+rjFCbvM+F6fA1y+AUYF4AUEEl44e+2/XGdSjfLoKd+ufp8WZwSubY/1Fi/7idjzUpu6gP5er0NPLFv7h43CdaqS9eY3T7jHPhQBr4bAQPjAkyK9feV8+5ld+J8eDWcJbt32DVLPW+rpO1OdtX9Oz7ngXFv7nGNXrLHgm/IP+7R49HBzHOU746Q/fED8r98RPXhBDZ/QlE/PW4MTDCACYhhDBZIyFECu9/AVhq2amDrBq7SsK3hT17DZJUvw6SHzapTJS+vOhOVHoSexPXYHPygHNcZEHpr8Pto1HGjYPA5QtdoSncr7mX3kMRRuafPj1lz+xgX4I+tpO0BzRQywa+nnD9C9ESux5OCvzc7Y2Ebg/pwjvxPH5D//B7lm0M0R8s73sAeNw4HynsranI09RUJjDNvpoONlNczzsE4rlTsnDZUdplX0FNS55rp0q9n0Cd+Ocu6Es22VaLNWnTu7PN7OUa6AfQkrsfmwADG+HqhwR7OPVUlDnSzvMa+AhypXPd1X7XBy9fZvpUvkB53AH8OPioS3aNHjyvRnGSo3h8h+9NHZP/+DuW7I5isL6F8EnAO9cEM2b+/hYhDyh+MA/AkBA8UWHDLlRmcgQUSwke/iEEMtTskta5se+t8xmLhIzryCib3Sl3uH1nlf15ROLl5/GYpPYnrsUF4UnKdQXlXgvf0GloZY76+nK0nBzjQfrLmXvbEOWvh7DWOZfvZexJ3d1jd/+uEfa9aoN/Dc3BduHbGtkePJ4LWMZfiDQzqzzNkP/+O7D/eI/vTB+iT7K43scctwVmH+mCK7H+9hZwkkOMUcpJCThKIJAJPfK9+q8gx+O8H5hdfj1u+R7lj7bhRycsVO+c8Sauhp0vokyWak8wvl6eK3TSDySsy5jErbtFfxnng4VTNXISexPXYHNpr+FoXszvN1nqquJaRIVt53DP4m/w3bdoTPvwPCadusvWDJnEmz2HzjNxUn+AEUo+nB1cb2LpB9WGK8u0hyt8+o/jrAaqPJ5Tl1ePpwJISB+vAIwUeKvAooPWoXQ/AY1qK+HSdx/65/zsW3C6NYEpCJFT6KZIQansIU56qdKTcebWuqGGLVqlr1/2y/X9vpvJQxyA9ieuxWVx3AN/NiDxRPLaPzq5JMB+GE/8jBltbhQOIxJnlEma5vL9lvWvAliVMllFgeU/iejwyfDUp6gBbNzCLEsUvnzD7f/6C8u0hms9zGsT2eFpwDs3BDM3B7Kv/YlKAhRJiGENNBl6hS6EmKeRW2j2X4xRiBAhBLSGOOZyV5i5++29V7ShbVgIB5S+e/9loYcsaelGQC+bxAs0xqXXN8RL6ZEHK3TTzZnFsJd7gYalzPYnrsTk432B6rZ6opxu46667rxgDBAfEdfrobg+McSrBWLc8si3Ns5aCPnvcPpz1JbrrnYe2KFC9fYv6999P4yQeIJxuoI+OiMjVvQrR4/HBVg1c2aA+poFrfTBD/WmG6v0xqvdHMPPiwV6/PW4Ozlqg1rDLEo22MFmJ5miOqlXkOoVuRbGLFES0+jP11f/zUJHx3bpGbt8LKSDSkMy7IgW5NUBQfKHCnVmvOwWv/jhF/fsxlR7fc/Qkrsdmsdozsw44IzfLp9gX1RqBrE3i4E0oxL2spiS3TUE36jXRZbk9UEXnwcM56mNcc/8TiXuD4uefYcsS9iEToP686/FY4QBbNjCzHOWvB8j+/BHFXw9Q/noAU9Rwus/b6nEBLH0nmMaQ0c0XbTK0YPR9L3gXHSDHXq3zvXWtWkfLpPNKYOt6AHwnmORgMgSPQ8ittKv6ckB37rdu32ZGOXatOrf4f39BczDrSVyPJwY/IHTGkN3rGmDMK3E3EPZ8r+HIadJpfc1cNU+S7iHhZatOh+sqccb0fUl3CGcdoM36+9870IIxH8Daz+T36HEeXGOg5zmq30+w/F/vYLIaPJBggQQPJHggwBSttwoF4+2yNQlbMZDoKl18Xqg2ZBNfa9jaO/gVDUxewiwraB+wXH+eoz6cozlawOQ1qW89eeuxDs4Qfbfyr/8JZ2DaQFtHEQGLHPxwftpDl4QXqHe+D+/MQ5JaFyq6Jq45IdyBtQu28px9+d9wzoFBANaBOZA7ZhrCbA9RvT2kqqcHgJ7E9dgcHGW+OWPIdXIdcA4mpFeX7h8xuUlQ0Hmz9mwPY/Qlf2+DmVuSeY3SWGdaItuTuDuBtdfa/zTpIroS6H4s2KPH+XCNgZ4VKHEMZy2qj1PIQQQxjCAHMcQgghiEEGlEpE5Jrx4I/+BAO5BtVQRN5M3W2ocjV9CLEmZRkEvf8RLVwQz1wbwjcdTz4/NFH4Cy0OMBoVXtDAVx685xesXFkjOa+PNGJGIQQY5jiLFX6sYJPUanSwwicIZvI3HXBWfgSQAeK7hxCjiH7N9+owmUB4CexPXYGJxzlPnWNOsPCoUADwMKvb6PxOQG4bSGrSrA6PX+gHEwpe5tDyETotu+dZU4pzVcXT+IsoXHCGc0bF0TkVuntJBRQD1Tqs+W67FRCBlCBglUNISKRxAyABinQaIuocsMVX6Cplo+iDJYZx1s3UAvcuB3Bz0vzukf8r1Ckp9WWYh2ss4rce1Ato3F8CqcbTSRucK78WVegZvl0POCen3KDZU7Mw4hFZLxC8Tj/c28JgCja+TTDygXn6l65wEc1x7nwJJD2RmV7rxfK2tSihcF+HEGkfiMuiQk18tWuYvDTqFjK+o1UxJcCfqZEjTxocTpz6Xoeu6u8g048/9tqWhbECbvZ7XTeehJXI/NwVpye6vrtcuziMSFdDEy9nRm9lsCU1XrN5dzT+Lu6wCac/Ag8CRzvcwxp4lE9GV5dwNnjD8Hr0ni7ulEQo+HCxEkiEZ7SLdeYbDzI4J4BCYkrK5QZVPk0w+YffoTdJ37cv17/m3hHJw2MJmFLRuw4yVdP7xVJ2h5tk/o7JJWuwKwU+O8Nu/KrizbMkuvuG0yB5FzDhkkGL/4Jzz7m3/d2OvWxQIHv/zfqLMTWKvhXP898JjhGg1jLGxeA9PlSvnwaRlxN4ERSJrsGMYr6nWrYEcQKanY7bpLQog47Mr9nwp6EtdjY3DWwdU1bFVRSeUaYEqBpylYGD6pCw8AXNPAFAX1hK0Ywlw0g0SENwKPonvZQ8ikBI9i8CAE2BoDfOfPl7KA073N9V3ANT73rWnwhUH0uWCCg0UReByT4tqjx3eDiEyYjDHc+Qnp9ivEo33IMAXjAtY0ECoBYwx1MYOpCzTVEqYp73rDr0ZbBmmpf+1hog155lBBijDd3uBLcwgZXT+apsfDhANNMACAvmQahlHUAVfSlzp6pc4v2/46EVEZ5GmfXXCq0AWS+uxaV80kJMUvVLf2cW8D/bdwj83BWVhP4tZVVngQQAwGREye0sy+c3BNA5sXcM16wclMCPAoAo/ie0riFHgSg0XhesfSOTpfioKUoB63Dqcb2KIgNXQdJU4I8DiGSFPonsT12AAYY2BMIEi2MNz7A+LRMwgVgfs+acYFuJCAs6iyE1LiZvZhkLgePXpcHw6kYhsL22hgUZ6WF3MOJthZFW+lBJmHigjfMIacpAh2hgj2JlD7YwT7k57E9ehxEZwxZDte5GsPyllH4uL7WSJ4g3DGAKwhIlOWRGQvGRifkrjotO/sHvUQMCUhkgQ8jNY7ltbCVhVMlsE2vRJ3F3B1DZMtKSttzYkEEScQgwGV9fbo8Z1gjINLBRnECOIRVDg4LTGEF2m4gAhi6peLhqiWx3e70U8QzjlY00DXRVeyxhj36wCpdb2a1mNDWFGxAUOqnVfoVnvjIMgMqHV7bc1TWhInRgmpea0L7CNDT+J6bA4ticvztcvjSIkbgsfReiV4jwmOwr5dVcPm+dXOk5xT/2AUgUnfF3ePsq6YUuBJAh6Fa5E4Zy1sWcIsl33g8h3BVhXMYgFblmuSOAme9iSux+ZASlsALhTYJS7FRPYCCBnR7/W4JTjqX3YWuinRlAtwLulewCW4kJcetx49NgIG7yRJvXDkZun75NreuEEEMYohh/5nvnyyM0YJHt93Vk/iemwMREiqrjzLae2zbi4e0DMpqTwrTiDiGDbP1jdZeAxwDrYqYWYz31MWXfirjFEwOgsC8DgGj2O4sro3/WQ8CCCGQ/A4ubTcs+3/c0bDlgVsr8TdGWxVQc/n1JdobRc8f1lfpkhSOs5tH+tTuVZvBVQWxIWikkJBgw7nLExdwOjqkbr4uS+WF/2aA9bMIL1PYFyAc0nHVAb+pw6mLqGb0h/Te/y5nIPVDcrFZ8w//YWqQlaIHOMULfOlQieCGEE0glDhXX+CHvcZbXC4FOQ2Kb3S5h0nmRJdxmJL2OQwhhgl9Lw1OfFLnnp3S/U41bdV9CSux+ZgfU9cWdGjqsCC4PL+KCHAGQNPEojxmIw+suXT6ZFyDrYo0JycgMcxMB5f+SdMSojhEHI0hrbTuydxPhuGRxHEeAKRppf37LUEzvfDmSxb2winx2Zhqwp6OoXJi7UcZZmUEIMBxGhEZkT3TA1+6GCcQ8gQQTxCNNpHEI8AAEZXKGYfUS4OYU0Dax7PpIe1BkzXsKahc9C5c1UdZw1MU0LX+YP6/IxxcBFARSni0X5nDOKsQTH7iGL+CUY3sOb+ViM4Z2F0hcXhbyiXR95Nk1M/I3yfEhPgXIBxCS4VuAiQTl5i/OIfexLX41Iwwb3CFkIOk1M1bfjFchCDBSvllEp2mYqr+YpdXMcDyXr7HvQkrsdmYS1sQ+WBJssgGAMuKbui5lQOkSRQ29uweY66LIGnQuIA2KKAPj6CnIzhrL3Yo8sPbLhSkOMx5NYEtshh8+zWtvXczZKS1MEkgUiTq51GrSWSn+WwZdmXUt4hXNPAWkvqeVl2ERGXlfSyMIRIUyJzadofww2CcwkZpggHOxhsv0Y42AUA6CqD1TWaYu7J9sMhMVfCWeq1qnJU2TG4oP44JiQYiEBY06ApF6jzKepiBqsfzvnGuIAMYoTpNpLJSySTlwAAZzWs1ajyKZWW3+t5LAdnNer8BHV+cs7/Uz8c8+WVVPZKdu+D3Z9ufWt73COsqmyrD3X6Mx4FEIOQYgRWQr/lyD8fJfR/w+hsLMcG0DmDOwfXUAajqx5ONVhP4npsHtrALBfQ0ymYUhBJcuWf8DSF2t+HyTLok2OY6ha28z7AOZgsR3N4CLW3v15fklKQkwnU9g708d03+PMoghiNqbdRBZf39cEb4CyX0NMT6sXqcXfwJZS2yGGmU3CvtF3Y08hYF3UhJ1tQu8/QHB3C9CRuI+AyQJBMEI/2kUxeIho+AwA05RzF4gBMBmAPiMCsA+csHByq/Bjzg7/A6ArJ+AVUNABjHEaXPifud2TTDygXh9BNcdebvTYYl6SsDp8hmbxAuvUKAGANlSdyGcA8+GPq6KvLahhnKVLBaJim7LPfnjIEOUZ25Y8rqpocnT4XSXQafB9IHxGwEhXgQ73BWxOdzcIZC9cY6EUBsyig5/lGcxZvEj2J67FxOKOh5wvo42OI4XCtvxFJgmBvD3o2Bf/9d5iiAMwDCHTdAGyRozk6IpdAreGUunSmiQUBxHgMub1NJZicdzNJdwEeJ1C7u1Rip9SV8QJOa+j5HProuCdxdw1/3pg8R3N8TO6nSXLh9yTzpbMsCKC2t6H392HLAmY2u9XNfqzgQvkB/w7CwTaiAZXeMc4hVQzG5eM0gHIOdTHH8ugNEQBrEcRDMC6g6wLV8gj59APKxQHqYo6H9L3AhYCKh4iGu4gGO90xNbqCDFIwLv09k+Ehfa6v4byqYWGd9QpqvVaZdo8HCm/7z0Rbxsg9caPn3BuKiHFK6tokhZykUH4px7QUSUClkRvsX2v7u6l9w9GEpbZwxhBpM5Yy67SBqRrYsoGeZtDTDM3h3I8/7z96Etdj43Baw0ynaD4fQD17ttbf8DiGfPYM6vgYYjymgWFRrJ0395BhyxLOWpj5AibP6QYYBBe7tHklzmYZxTMEARmD3NG+EsMBwpcvoba31wqAdlpDT6eoPx/A5vktbGGPq2CXGZqPHyGSBHJ7GwiCS3+fBwHU7i711B0fA/zjnU4kPBZwSSRORaPO1OSpQNc5ysVn6DpHPvtIbpWMwVoD0xTQVY66XOChER3GJVQ4QBCPV0xNevR44BCcQrTjEHIYkUvkMOrKHsk10huNhAosVBS+Haysh4oMS+TNuZu6xsIUFUxWwixLmEWxsiyglyVMVsHmFWzVwNYazec5rH4YY8+exPXYOEhpmYEfJnQt9P0AACAASURBVBQ3YIwPbb3EpTIMIYMAcmeHBodFAacNrHk4ZTPfClfXlNe1WMDM50TSpLywpI0JAZGmkJMJ5HhCaudyCVvc8r7ykQhyNIZ68RxyPAbWcKW0dQ09naI5PLz9be5xLkyWof70CXJ7G67xBhOXqcFSQm5twWmN6t1b8DSFq6q+N+47wYWCioYIouGTI3FW16h1jbp4XKou4wLSkzghe4OPHg8ADGRcI74M1/brgoMF0vewxVCTAeTWAHLLq2zb/vmAyBxrDUY2SNQ6l2vrSFGzXm1r173aZsoaZl5Az0hl0ycZ9MkSzckSerpEM81glkTiHiJ6Etdj43DGwGYZ9GwKvZjDZBl4GJLhxWVgDHI8RvyHPwCcw1YVbF2R+90TgFnMUb17BzAGEccXq1qMbqw8jhG8eA5T5Kjevr11QsQTyguTOztQ2zvgV7lStuHeyyXMbAYzncFWD/PG+dhgixzN4Wfo6XPYPKcsQl/Wey44p7Le0RDBi5cwGSl5zefPt7vhjwrMuxgOoKLBkyNxjxOMiHmYIohHEL0S1+O+wxuR8Djwtv0rGWxpBDEITzPY4gA88o9QUV9bpE6fB5eYZH0nnC+FtFkFk60obV8u8wq2rGFLKpm0lV+vGvp51cDVD0N1Ow89ieuxebQhzvM5zGwOs5gDbEy5UhegnfGXoxHYTz/Bao3Gl9vZpn4SbpV6Pkf9/h3EIEWwvwfnS9q+VEO6vqQogtp/Dts0MPM5mqMjKqm8jZI2xiAGAwR7e1C7u5CTyaUZd4Avs81zmNkMejaj86LHvYAtSyqNPJnCLBZE0IW4WA32JA7pAMHz53C6gasb6OmUlPcnMvGyMTAGxgSEDKDCAWSYgrE+0Pph4zTzT4YpVDi4sl+4R49bgydrVCXF/OQw68xE5CiGnAygtgeQ2wOo7SHU1gByO4XaGkAMY/pdubn7lFsdu6wqbc4B1vdc+qWtiIjpaQ59soQ+XqI5XqI5WdD6yRL6JIPJKv+d9LDKsNdFT+J63BicJ2Llr78h+uknyDVMTpiU4EmCYH8fyb/8C0Q6QPX+HfR0ensE5Y5gl0vUHz9C7uzCZBmYVJeqIUxKyMmEyldPKOvLzGgQfpNgQQCmFIIXLxD/wz8i2N+/tIyyha0qNB8/onr3FmZ5s9vY45rw15WZz1C++Q0OQPj6NcQVPY5MSuqhYwyuqgGj0Rwf3wvXVACdas04764jp/W9I5lkq594BS7oQpN7PFwwISCDBEE0JLt93h/THvcEjHlyNqS+tWQlKDsJIZKAlLgoOFXb/FLEpLQxKW8sh80ZSypZUVO/WkaKWqe45VT+aPIKtmhVthqmqP3z2j9vyMDkEY8bexLX48bgmgbN4SH1eI3HCF6+7L7ELuu1EVICe8/AwgA8imDLgnrk6poGYLd9QbJz6rlvwMTBZBlMUSB4/oLUkCgmNeS89wflxfHxGEwImNkMNs9RWQuTZTdnMsE5eBiCDwYIXrxA8o//CH5Z6SdWeuHKEvWnj6jevYNZLje/bT2+D85Bz+eo3rwBDyOoZ7ukrl7RG6e2tiDSlK5PY+AcoGczP3t6B2Rp5XohlzTZZd+1YdL3zTGPeuEGkOEAXAZgvFfhHjo4l1BhChUPIVTYH9Me9wcMkNtDxH94jmBnCLU9hGyXIwrb5q3CtkGeduoY6f9ZWToHujc7B1dr6FkOPcvQHC+gjxZojpZojhZojhdojhZUKrksHzVBWwc9ietxY3DGwCwWaKSkfpujI/AkAV8jN45JReV6z1/AGQO5vUOvcXICs1zC3YY1PWNgYUg5aOkAYpACxsAZQxb5rTq4KfhBb3NyjOLPf4FrNMIffrgyZ48FAdTz512fElMSejqDmc82d4NjrLOfD1+9QvDqNcLXP4CF4eV9cPCB0jll4TWfP0Mf99EC9xU2z9F8/gwxHkN93gfjgiIH1OX9WYxzqN1dKh+LQohBCn18jOb4mMhdc9Ph1L4UKAjA4xg8SSGGA4jBEHI4gAOgj45IJZxOb2F7rgcuAgTxyBua9F/LjwGMSzKpicfgou+F63F/wDhH9GoHo//z77vetq7HLQxOjUg2DUM2/7byqlleweS1V9Ro3eYVTFHB5jX9rP29oobNa/q/gnrZnjqBA3oS1+MmYS3MYkFllYeHaI4OodjumiROQsgBeBBCjsdQz/ZQ/vILKsYp1+M2SIA3D5GjEdTePoK9PSIkdQX27j19tk3b+jsHfXyC8s9/BleKShWvInFKIXj+HHIyoZKdNvtrPiNVYhM3OsZoX0wmiP72b5H81/9GpO6KPjiASJyez+kcOPh8f0rtenwFW5DqLUYjInNxTL1vV5A4CAG5swO5tUVmN1tbKP78Z5iyhHXu5hV0xgAuwCI6R9XuMwTPn0Pt7UHtPYPTGsXPP8NZS5/xninBQiqoeAwV9yTusYALSSY18aiPFuhxv8AZwlfbGP1ffw+A3UiA9nlw1sLWDfS88D1sC9RHpKzpVmU7XsDMC5/ptpIV3PO1c9F/W/S4cTit0Xz8iEIpOG0g0gEgJSDlxWVa7c+FAI8iqO1twDnI8RjhyTH0bAabF7BFDluUZMygG5pht5bKpewXtdC+J4EJQeqRlOBSUkC1VOBBQMpbGIJHYac8tS6MIh3AzGZoTk7A1M25LtmqpBy1T59Qvn2DoKkhx5MLjWEY7TBwpaD29gDOifju75O5zHwOk+c+tmHNfiC/33kUQwxSiOEQamcX6tkugv3nZ0ooLzqGrlUtp1OUv/6K6tdf+164BwIzm6H85RfAWkRCkJGJlBeqru054BijUlv4nqCdHXIinc1gPEG0ZQVblXQuGnP5tdrZWvuySKXAlerW6XqNugkFnsQQcdKds2IwBI8T2Dy70KTlzsEYhIoQxmOoJxgt8CjBGIQkdTWMx70rZY/7BQYav1xSKr8unHOAceRK7nvTbFnDlM1pf1rx5fMVBa7tdVvtc2u8OVZP3K5ET+J63Dic1qg/fYLJc/DBAOGrV2RhvkYwNDinDDmlIEYjuNevYZdLKtM8OkRzdAR9MoWeTmHzHKYoiMjphq7/VilrB4RC0EAwCMCiCCKOweMYIklp4DcaQ45GkOMxxGhEA0VvLsI4R/X+PYWQy0vs1793f5UldFWh/vQJYkiOZjxOLnX3BABICfXsGeTWFtT+PsL5HNX7d6jfvUNzdITGG8O4q/rlGCMlNE2htreh9veh9p8jfP4can8PTMi1jEyctXA+E6769a8of/21z4V7INCzGWxJ8Q9yZ4dC5T2ZuhTetVSkKeTODqI//D2VMR5+9uWVJ+RMOidS52pynm0JXfsa3bXakjc/uSKSBMJPrPAkgRyNIEZ0rcrxCEwFtI3ta/iBii0L3Np083XAGBjjEDJEkIwRPMGQ78cHOqYUFzFCkPQh3z0eMZw3Imn72KbZaQbbaibbyZLy2IoKTtuzYxGHlXXXk7droCdxPW4ePuAZyyXqd++QhRHCly/J6EQSIVhHkWNCwHEOxpifhQ8gRmOY/Qw2pwGh9eYKZ2b46YX8rD4jEiI9mVMBWBBQL00YUj9N+4iiU9Wu3Z5WIQANCW/sXuMc5ca9fet73ULAGBq8Bl8MCFqzGL+fwDnEYECDX84gBwPoxZJ6CauKjoXR3rXJwjm/n4VXPISkfZum1Au4Qmp5EJ55z3M33e93M5uh/vAB5a+/ojk6IiVw0+WnPW4ENKtaQh8eovzzn+CqCsGrV1Sy+8U1sYo2/gIAnbfOQUwmYFJADIeQu88oNqQsYOsGzng1zp+L/lU6BQ6cgXEBpuSpWh74vo0gAI++vl7Pn12+XQLHuADjElwocKkghKJ1EXQ/655LhXi4h3i0Dxkm5xpgCBkg3fkBjAuYpoTR35+vqKsc2cl7VMtDbziwubuZUBGSyQtEg12/L77f1EPXOcr5Z1T5CYyu4czt9jW2n4N3x/L0+K3+TEg6xmG6hXTrFZnVnEPMGRNIJs+x8+P/Tse0+f4WAasbZNP3KKYfNn5MexAYF+BcQgQxZBBDqAhCReBcggtJ5wkT3QQSHFUGOWtgrYE1NUxT+WNe0Lqu4ez9jFGy2sBV+tT9sTx1g6SsteZUbftCWSNHybL7ma01XH0H5nSPGD2J63E78IPCzpnQWsjtbSrLu471slfmRBCADwZQzwzliFj7tcrkn7flhl+6PHYDztWyglU78jsuvzKLRadciSQFkxKBlMCXJO4c8DAED4iIKW8OA2Ngq6ojcqdugpa+mJQiYxT/t+3AuVNF1t0fjkor9PExij/+EeWbN9AnJ3fjLNrj2+BLkpvjI1jdwJYlGfzEMdCSpXXAGClnUQS5vUOv6w18ukGmdyXrzo0v3VhXSZnPMqJrd6Xk8h5cr6dg4EKCy7DLfFNhemYpA78eJJBBDC5DIgNc0Of6AkJFGO3+LQZbr/1++353zWL+GQd//h+o8xPAmrMZTd8JGSQYP/8nbL38Fwj/2b4X5eIIR2//P9gDDRRz6DsgcUKGdPz8sVT+WJ49tkk3uCdiJ889powLpNs/IB7tb+yY6irHpz//D5Tzg40f0x6gtgUhIVSMaLCDcLCDMN1ClGxDBBFEEEN4Yg9Gk9POGljTwGgib7rKUBdTVNkJquURqnwK5yzMPSVxrjEwWQl9skR9uIA+XqCZZtDtY5Z14do0FvP3d7ua7WZP8976U3Kj6Elcj9uDtTB5DmcMqrdvwJSE2tuH2tsDD0OvHF0+EFud6aeB5MWDg47QXRFrcG/RliOenKD89Vc4Q2Vn6tkzGhhf1CP31T463Rc8juGaBq4tYfODafZF3xH8sbhOOK3z6qeZTtEcHqJ68xvqjx9hZlMqm+sHFA8Otq6B+Rz1x4/UW1aWUPv7FO4eBJeWRH+pol+Ii86Lh3a9enAhEY9fIJk878iaUBGEDLtZe6HC059JnwsHXJwJybj/mytKqq8BXRdeIdr8fmacQ6qI7PVltJGeMNNUEMrHL9z6ucEQDZ8h3XrZEXOhIkgV0edbOaZcBkRcW/XxkioTIYON9ssxJnzpZlsr0uO7wTiEUJDRAEE0pLLneAwVj6DiEYJwAOWjQbgMvBonu6xHUuE0rNFE5poSQTJCmG6jGe2hKeao8inqYoa6mEOXS1ir4ez9qFphnIFJARZI8ECAKQEmuA8IB03aakMqW2PgGt0TtVtET+J63CpcXcNojfK336CnU8T/+E9ERsZjykTbNB7oQHAVej6HrSrYIqdZLjiw/edX98h9Cd/rxoTolA+KaXGnasfq45pwxsDVNeqDAxT/+Z+o3r+HPj6CyYuVUrkeDwpevW0+f4atKujZFIkx4FICw+GVYeBPEVwGGOz8iJ3X/xtEEEGqGGA0IcJ8DxwYB2P8VFF8dHj4990zYAzp5CV2f/pXUttU7I9nW7lxejzb49vjMYCUfhEmSCYvkG69QjJ5iXj0DJxT+SyV2fKOtDG035/tRCo553IZUGlllMLZLThr4KyBbgpUy2MU809YHP6KzL4H6gLmvpA4wSmCwFgiaZYYWjteaCuYwBgpctrgNAOux02j/wbucbtwDjCG+rPqGjyKAc4R7D2D3NklA4U1nA/XwYNT3i6C1rDGoDk6Ag8CUuTKCnZvr9tf8APEi3CmHG315xvYPFtVcHWNZnoCfXyM6s1bVO/fQR+fwBYlcE++jHp8I6ylY3xyAgDgQQhXV5C7z6C2tsDThK7jb3U6u8HrtFWgnTFn1OebBGMMUsUIkgmpNcHVMRyPDc5ZGFPD1GU3mDslODTAfWj3Z6EiUmDCFCKITtXTHo8QRN5kkCIcbCMePkMyeYF4/BzRYAdBMjk9n9d4rVMX/68nqqVOwEXQBcLLIEG5OEC5OILRNaypN/zZrgnOwSQg4gCwCfU3JyHkJIXaG3ellGZZnrpL+t4323yxXFHrbGMA00/ufi96EtfjTuCaBsYYVO/eojk5RvjyFcIff0Tw4gWC5887g4IeHs7BLpfUX7ZYwMzmCOZzRH/zN1BBcGpqcgewRUExAr/9ivKXX9AcHcF49fCmB8w9bgnWUt7fdIqi/iOaw88IX7+Gef0a4YuXZHhznd7W28IqgatruLrx2UM9bhLOWpimRFMu4ByVbHPxhfFDjx73FIxzcBkgHGxj8uKfMdj5kQh8RCY1myTwTAioaAChQqhohGT8ArNPf4RzDnU+RV3cMYljpMaxSIEpATGM4YzPcNOmW3fadARNL0qYZQE9z2EWfjmnpZ4XMIsCWFKGKGwv2X0PehLX426wosiZPKfBljWUI5VlEONx5zjHw/DU5v8WB4ldQ25nCFJSpMHxEUyekQvjLaJVErQx3aDaGQOTZd7WPQELaX/d1L5yPrjZNY0Phs5pn3w+RPX+Perff4fJMop56HvgHhecg6sq6Lomgu5dZ21RwCwWne0/5SxGG8shWm/T6FxrrxHnt9GWJWxRoDk6Qv35M1UANDdriOGsRZ1PkZ28I6fCNXqehIoRxENwGZ6WWa7AGo2mWkJXOZwzG+mXqZbHaKolkazvfrWzsKZGMf/cKQxCRuDCO3a2bpUr5aQMDDJMEcSj0xK1ezUh4FAXc+TT330vY3DlhIWQIVQ07FS7Lwf+zlk05RJNlXWldd9bg6brAk0x9/fe/v57fZACp6IBosEzpNs/YLD9A5Lx89PjvgLn3Jl+N2saOEfuzP6OdFpm27rVCgkmFLjvN6eIkcA7m4aQQQSjS8A5LI/feTVOb9a90scCuKoBpCBn6sscwhkALsDkxZMvriVkxkJ7V0qzJDJnFqfrell26p0ta3K+rDWpc2cUPD/5Zq6IRHri6Elcj7uFJ0p6OqWem8NDVG/fQO3sUD7Zs2dQ2zvgg8Htq3POwTbkzKc9eavev0f9/j30dHpndvm2rqGPj2GLEs3JMaq3bzsFU+3ugG1t0766CWXOORq0L5doPn5A/eEj5fUdHsJkGaw3rulvuo8YzsFWFeqDA+jFAs2nTyjHYwT7+2RUtLMDtbtLJdG3pQ63ZZNVBbNcQs+mPpfuGProGHp6ArNY0uSLvlkXOGsaZNPfYU2ztr1+PH6O8d4fEKZbYIKc7c6+Zo18+gHZyXuYuqRB3ndC1wXK5RENOjfcs2qaEsujNygXn33fWEvK2Omg1htAtEYQg50fMd77ewTJGIKxr/bBncI5FPNPOH73bx3JvAphuoXR3h8Qj/fBuQITX5A4a1HMD7A8/A26ixj4vvumNRr57COs6+/B3wLGObhQiAa72Hr5L0h3fkSYbkEG8QXqsSPFucrQlAs05RJW1zCm6Yh0G0shgwQqIhMUFQ0A/nVPO+McQkVIt3+AikYAY2jKOZoqg642e99ytYbJKvA4AI83ZK7DaVJGJCF4KCFHyalCp9uH7copbV5BL3LKl5vlMD5nTs9yUu0WBVBrON23ZFyEnsT1uHu0g6+qgs0y8JMTmPkcJs9p4DWbU/B2GIIHir7823y51vp+NRrgPGOOldiBLorA26h3S2Oo9Kotv/JqkylyInFHRxSafXh4t6WC1pLCUFUw2dKXLpawRQ6zWEDOF+BRCB5SoHprZsJ8hlyXwQWc3U/tvmn3Sav4tepG08A2NcxsDj2fo/7wAc3HD9CzGfRsdjv7w2+XWSxQvX9P7onnwZfR2SKHns+/7z3b83OxQP3hIyAuvm22QfR6PjsNr/7G92zLVOuPH2nQeIFTqJnP0Rx+hpnPYJtbKr0xBtaTdrNYQJyc0HqW07W7WFBum1fRV8/BM+fhZdfr6rW6er16F1RnjS/nMd25abOcSNx0Cn18hOaE+jRNlnUxJDcNaw2q7BimKQBc3qt6+nmBdPICQTyC4/KrXlVrNKrsBNnxO+gqg67zDWynhq6yGzEdctagKWZoitkFv8E68tYuhQqRbr2CsoPTaJh7hKaYIzPaq8tXl9PpukA82kc02AX41/cM5yy95vR3UlnLDN9L4pyzaG7omD4FCBUhTLaQTF4i3X6NZPz8NCYCrfJm4KyGrnI0dYammKMuZqSqlgsYTYpcewxOSVzsHS5H3uFyBBmk1AvH2ggfilAJ4hGkilHnJ6iLBYrZR5im3JxjpXWoD6bI/v0NeBJBpCG4kuQ8qSiXk0u/Lr0Cxy93+V7tu2cBx2X0oo0bsGUN7csszSyHnmXQU7/0JM76bDrXGNimJYRtb107Xnu653tP4nrcKzitYZ1Dc3wMUxRkbR5FEEkCMRxCDIfg6QAiTcHjGCKOu7BuphSVXX45QGwz5FbISUtMWlMOW1dwZQVT5DQQzJakLBUFPaqKfteTp3uhNvnSRpNlcL//Dn1yAh6/gUgTyPEEcjKGGAwhhgNfmhpTaaq3hj9D5treIT8gdnVNRLEsYbKM9sdsRk6ZS3re7Zu6vjVC64yBqyrUv/8OWxTg4UWmEa6zPtbHR9/3nlpDLxawTUPk5OefL/ndhvo9/e9/+5u6Tp1uDg5Q/PwzLrKhcU1DJD7PYfPvH9xfdztd08A4B/fxI/R0iiqKweMIYjiia3YwoMdKuSULQ8olbCdjVvvp2mu1JWsrkyrt9UeP8pQ4Zj7Mviy7DETb5iG2Ey63db06C9NUFGS+ZuWArjNfinXBNjoHo6tuxl9Xy+/fTF8KdjdwsNaAOQtrNRhj3SDV3VMCYnRFg+g1j6mKBrC6pv18wXE1pkZTLb2Ks/j+c9QB1t5uft5jgopGGO79HYa7PyFIJl51XSHszsLoGrpcYHn0Bsvjt2jKOepi3pVUUg+o9Xz8y3JKSYpcPEI82sNg928Qj/YoaoSvqmGkVMfjF2Bc4oQB5fIQ1rmNXB/OWOQ/v0dzvISIQ/AkgBzFkKMEYpRAjhPIET3EMAZPQ/BAbq4SisGTPQU5ZhBJCLs9/LqksmooMHxZnvbTtSrdPIeZ512w+FPtretJXI/7hbbnSuvTASlj4GFIA8LRiIjJwBO5tgennfEPglNy0vbjfEngVpU2Pxh0VUWqW5YTaVnMYZZL2LKkjLP7Cp8lZ+oaZjbzTlIScmsLcjKBHI0gxmMaQMcJeEQD6C5qgJ0lcdCa+pxaEpfnMMsFqUsnJ9CzGe2Tqrqbz+scoDXMbEaf9zZgbacU39p7OteRMn30fST0psDYad+S05ZU8/m8mzwRwxHEaATZXrdpCuGdLFcD5SHkVyQO1jfMW1+C88XEAj0KulaXS3rv5YL63e6BmY6z+lrhvUQQLCj04+vBiIODMxpGVzBd6d0Dh7Oesxg4kNp4XwkcQOridWzfTVPBXtrnRvdc0/hjWn9/OWWPb0Pbrxam2xhsv0YyfgEVpmcUOCJwFarsCMX8APPPf8Xy8K9U6ngNZZwLBRkmRNr992802AHn8oseYo4wmUAGMYr5AYJoRIptXeC7zxPnUH+aof40o/y3UEFOEqhJCjkZQE7S08eYiJyI6X5NyhzvFDom24xZ/sX2X4z2d5gSgLqkz85YmKKCyarTcPFpBn2SQc+WtO5NVGytqVRTf1G66c1XHqsTZk/ietx/+N40LBbUNzed+hItRXlVog2fFKcOeWzF1tfRIOjLUsozpM4YmvlvGtjWyc4bhzwotOqcd4fUJyfdvjotaeNn95P/O/jGZGfbmx7VotvG74tW2Xho+6THxtGFT4vAi54ORpcwpurOJVvkNBmzXIAdHvoBgKIv/jMllby7XgH4v2+XdsVgyJ+TqxMxXZlvc28IXI8ePR4WZBAjTLeRjPcRDnagoiH1prbwClydzzD/9BfMP/8FVXaCulzAmuup2dYa6kddfIazZG6z9fKfKXtQyDO9d0xICMZo27ZfUVllqwhvCE4bWOegjx31yB0uwD2xY4EEjxR4qCDSCGIYQw5jiJFfDhPIUQwxiCDSiIjcJvugOaPt4Bw8VFCTFLbWsFVDal3VwJY1bNkQmVtQCaaZF7Rc5NCLEnZZwJYNTeY/snmSnsT1eBgwBtYYoHwEM9A3iW4AXQBFgZ5u9bgJMC4RhCME4QDOOljboCockTgP15Y1Zne4oT169OhxBWSQIB7vIx7tIYjHkEF85v+tNWjKJcrFZyyP3mBx8Aus1d9GppyF1TUqo9FUOaypEQ92EKU7EEECHhAJohw6Acc4wnSCdOsVTFOSGdEms1f9xK1pDHDevZozCvxus+EmKeTWAGqLlnJrADVJIUYJeCi9bwEnx8svHmR8sr5jMWOMqjWUhEi+NoIB4PvkNMy8QDNdQp9kaE6W0CdLWnr1zmQVXKM7Za6LSVhdbycQHxB6EtejR48ePa4FKUMMRi+RDl/A6BJNTTbpdfWdJjI9evToccuQYYpk8gLRcPerGAGA3Fbz6XvMP/8VVXbsCdx3qv6+J7WpMuTzT5DREMnkBWTwdZ93EI0w2HqNanl8+xmL1sHBwhY1tHWwWYXmaIEqVF6lC8AiBRF5tW7gFbtBDDGMIPxSDmLwiNS9TbqMM8EASIhhDBZIyHGC4PnEq3TNqVqX113sge5iD7xi52MPXEOGKQ8JPYnr0aNHjx7XgpAhksE+xtt/i7qao8yPUWSHd71ZPXr06HENkNOoigaIh3sI0y3wlTJK52MCTFMin33E8ugN6mK6ISXMK2B1gXJxCBmkCOIhMNz96jdllIJLheBwDC4UTFMBuMXScesokqDWFNR9HgTvzFHU9hByewi1PegeZntIBC8OqBeOczDOVpZsxZCOXnKt/jrOqbVQCYj0a7Wuza+zVUPRBdMMzfES+mSB5njpHwvok6xzwrRlDVs1D0KV60lcjx49evRYE8xnKQVQKkEQpN5Kv0ePHj0eFoQKIYMEQTyGigYQKjqjdDlf+tiUS9T5DHU+9QRqc7BWo86nKMMUuv7h3N9hXIJLBhHEkGEKa5ouEPzewDrYokZjHGzZoDleoooU5dBFCiIOwOMQPA6IzPk+um59EEEkEf1+sGFq4ssy5TAGDwTEMIbdH3f9dLaoYcoaNq9g8wrZf7zDDqYxvwAAIABJREFU8n++gavvyr13ffQkrkePHj16rAXGGDiXkCKEUjGkSsC5uvoPe/To0eOeQcgQQTyCioeQQQIhzyo5zhropkRTLX0W3GLj2+CMRl3OIbKYYjY8MescHH1vHLiADCKoiCbOrKnh3D0q/XNE3lA2MPPz3TpZKCFi31u3M4TaGUL6pdoZQm0NIEYJRBKeqnOrRnUr621JJutku/M3i7V/y8kJUwwinPeN5RyVipq8hG0M8p9/h+lJXI8ePXr0eCxgXCIIhgiiMXg74Llvqcw9evTosQa4ChEkE6gwpUH+F3BGoykXqPMZjL6ZqCHnHClrTUm5gkb7ssKvt4fLEEE8hq4y6CqDe2DWZU5bmKImwlc1aE6WEL8fkzrnVTpaJ9VOJBcs4wAsJNfMTX7/sEBCsMhn4m3udW8SPYnrcYfoPMUv+b+LsOkygnWu2O99zy/f46Y+90X7dZ33v2gbvvWzX+dOuIn3eAjn0ve83/dcM9/z3vTaQigE0RBhPKZZa8ZAc6Gt49ht7+sePXr0+DYIGUIlY8jgfBJnbYOmWKAuZrA3ROLgMyCtrmB0DWsacKhzt0fIECoeQeTT04zXhwTvAqmrBpido9YxgAkBHinKqNvyPXVbA8ht74TpH2IYg0sBx70y5/wLrCzOw0V9dowxMl0JJFioNmq+cpPoSVyPW4WQIYQIMRi9QDp8gbpeoiqnqMs5qnIGISNE0QRB6MsbRNDdzKzVsKZGXWVoqgWqcoa6WuBbB4ZcBAiCAVQ4QBAOoFQKLhS4UN09wRpNN/I6Q1NntJ3V3Dc2r/u+DCpIMNr6CUm6h6bJ0VRL5NkBiuwQXAQQMkAYTRBGE0gVQcrQhylzqst3BqYhF8CqmqMqptDN2ZsgFwFGWz9hMHqJupyjruZo6hxNkyFOdhAnu+AyAGcCTZOjrhaoiimqYgoHC6kShOEIUbKNIBx0711Xy868oiqvCLtmHFJGUEEKFSSQKoVUEYQIwLkC58LfHCmAvT2m7XZW5Rx1Oesayi9+GwkhAkTxBOnoJaSMUJUzehRTGF0ijCcIozFUkPqyPwHGZZc9ZnQFrUvUfn82XYnKdRvGGX1efx4FwdCf5wpgHAwM1hlYo2F0QedRtURVzqB1cWVfgxABhAyRDp8jHb6A1mX3OatyBiEUwpVrhs4dCcYYhURbDa0r6Kbo/saYGtZcPCjhQvnjOEAYjWj/iQAqSBBGE0TJNsJoBM4FwmgMxhiEijCcnN/TAYA+czFDVZ6gLKaXvn+PHj163DSEDBBEdN9k55AiZy3lX9YFnL3Z0jqKxTSwVp9L4AAqCRQyABdybZv+BwVH+9zWGnpewDUGZlGgOZh5lS48VeqioOu3a10y20w7+llwuh4qyikV7MGQs3XRk7getwgGKSME4RBbz/4J+6/+O7LFR8xPfsNi+hZaVwijMUZbPyEdvUCS7kKFw84tyugKdbVEvvyEbP4BmDqyNncW30LkhAgRJds0OG7fL0ghZdxd6Lop0DQFiuUB8uUBFrN3aJoctg0hXudTMwYVDLCz91+w8/y/osgOkS8+wn2yKLJD+iIJhxiOX2M0+QlRsoUgGnUE1loDoytU5RTF8gCL2XsiIF+QOCFDbO3+A/Zf/yuWs/dYzt6jyD4jzz5jtPU32Nn7Z6hgAC4ClPkRlrP3mE/fQDcFrNMIozGG41eY7PwD0uFzcCFhTYNs8RGL+e9wzl5O4ph3+goSJOkzxINniNPdjmAoFYP7z+ScJXLRFGjqvNvOxfQtmnoJWHPp/uVcQgUJBqNX2H/9rwijMebTt1hM32DuHKrSIRnsYzT5AXFK2yFECClDWGfgnPETB3Mspm8xw69wcGis/iYSF4RDJMN9DIYvkI5e0OcNUnAuATBYU0PrEmVxgnx5gGz+EdZqGFNdef4KGSIIh5js/gP2X/13lMUJFidvMJ++gdEVVJBgOPkBg9ELxOkzBOGIiBwX5H5mKlTlDEV+jPnxr3BWo67dpSRKiMB/pucYjn9AnOxAhQMi5jKi4+gHPWE8RhiNMRi/upSQLhcfMD95g/nJr2jqvCdxPXr0uFNwGUDFI8gwPr+c0tLkqW4K2E1ms50LR5O1pjnjkHlme1sS579XHiXOOGHmIHkOpz1wDAAYmBRgUpAj5jiBGKeQ4wSyXU4SyFFC/zdKIdK2t/AOP9sNoCdxPe4ADJyRKiJVTBko41dQ4RBBOEQ62IcKB2BMwJoGzlowLsC5QhiNwBiHlHH3t/nyM4rs89rvroIBKVODPaTDfa88DaFUAjAGY2q0N0jGBalSK2pLlGwTkVx8onDjNcgcA6NZNK6ITKgYQTBAGE0wGL/GcPIaUbSFMJ5ACAVrNOAc3awZJ/KhEtQyJnJ3zqwh3ec4OFeQMkYQDsFFgCjZQZRsd8oeFwphNAZASo3RFQCHwfg1ksEeVJAAcLSfVYQ43QVjHLpeoq4W0E0BfY4joZT+WA5fYDj5EXGyDRWkEDIC5wIOoH1rHNp2ZCFCminjkkiCShBGY2TLT0R0LyFUzNtD0+xkCBWkiJIdIr6mRjp4jnjwDEKEgPOlMU17LgnIYADOiVSqcIjl/D0W0zeoyhmdd1eQOcYE4nQHUbKLdLiPZLCPMKJzmDFPoKzpziUhQ0TxNoQI/bEfEaFbfkJVnFyhPjJwxslURMYIwhEGo5eQKoEKEqTD5wjDEbgM4JyF1lX3ObkIqYdNBBBcIYy3iOxO38CaBtY25+9dryICFOatm9yTbwOhDB07Lrya2sDoyl8756MqZmjqDEZX30CUe/To0WOz4FxCqnZS6mtSJIIYyeQlhAyQTl6iqc837NgEpIox2P4BUkUXKnFgHEz4UstHyuHOwPl/XPfk7DckA6w2sLUGq2rYQsIoQeHinNEvOwdnHJwxkMMYQt5yzt4NoydxPe4UVKJFpYzDyY9QKoYKh3DOUIldnQOMQQjlB/kpVJAiTrYRxhNE8Tac+58+o2q9frEgHGK8/XcYbf1IpCUcggaqGrouoD2pacsgZZBAqgQJ9hCnuxiOXuHo4N9psG81nLtGmQVjNJsmQipVi7cw3v5bPHv+3wAwwDloU0LrHAw0aBcyggoSCEElepzLc0nc6SdknboXpTtEYqhOAc5ZMID2ZTgAYwy6KcC4wGTnDwijMYwuqS5fKAgRIYq3oVSCqpyiLE5QZEfnkrhWgRtOfsTWs39AGI7BGKfZRT/AN7qEtQacCXChoIIEShI5jt0zRPEWksE+8PHfUCw/rz3YJ8IZI053vOpGRFWpBE2T00yqtnDOdMRHygg8GCCIRxiMXkEFSUcyGmuveG9yaUwGz7H97J+QDPaQDPa62nytSyI9llQ2LkMolUDGif+Mz5AOn2Mxe08W09X8SvWxBRcKKkwhVIjB+BWVwUYjMDA0Te5JkgGY6D5nEAwRhiPE8Q4G49cAgDI/RtNksPV5JA5dz4U1GropYXQFLqjkVFkDFnIwxmF0Dd3kVApbXezeVhUnVELaFD2J69Gjx52DCQmhIgr4PofEySDGYPs10skLmsC6QUt/BgYmJLi4WGVjnCZpGRenroxPFYwy5gDAWQenqQyTlQ2sKGE4aILdWjhtaVI8VBDJ11lyDxk9ietxp5AyRBRPYK2BcxZNkyNfHlAPWpPDGhpg0oB/gDAaIx7sIghHCMIB4BzSbJ8GpPUSTZ1d8l4RVDikfrzRC4TxFsAYquKk6/fSTeGVKQCMQSpS/OJkF3G6Q2pgskVlZsUU+fIAZX58gZpxHpj/LCnS0Yuu9yjPPqOpMtTVgmrwvaLBPOGTKoazDapygSI/PN3G898CUsUAgKbJURUnqKsF6mqO/7+9M2tuI8my9HH3WLFx0ZJKVXZWtk3PTLV1m83TvM2Pnr8x7zPVZd1VlZ2rNi4gEKuv83A9AiBFUqREkYR0PzMKIEUCAUQA8BP33nPSbIaiPEAxOURRHiAr9rDY/x7O9XCmQ9Wv0TWncM4gLxbIywOU06dUOcwXKMonV56NdFaj71bommO01T50t6Kqne3G59U7ixDcWBFMsxmyYo7J9CmKyRNq95RJ3LbFKKo+JNDJNXFKswIQCMGha06w1r9A92sYXccWWI8kyZEkBYrpU0ziY6Mq2QFm82/hnYb3r68UN0JIZPkceXkQj6UXUCqDi+2SbXMMqxtY2yJ4j4AApTIkaYm83Ec5eYIkLZFmU0xmzzBbvISzPbr25FoRNKASqqyF4CAgYG2P6uzXcVbUxkqXEBIqLZBlU5TTZygmh3QyJJuinD7BbP871OtXMKZ5r5rsvYHpK7SgNmaZZADoNVRMDqNQzqBUCmtadO0p6tUr1NWbK7fb6gZa1zB6/dnnSxiGYT7E8DkkZHKpKBJCQigJXNHeeN+ImNN53UncnUTE9shUQWYJRJpAZgoiSyDT5NylyJKt30kg8wQiSyHzhGbg4nVyr4w/y1PISX73+XOPgC/vETE7xTBfQ7NRNdr6CCfv/oKuPoFzPXw8Yy+lQpKUmMye4ck3/4rksCQTlEmKyfwb9P0K9erV9SIum2C6eIHZ3neYzr9Bmk2h+zXq9WucHv0HqtVv8FbDjwtMqgCqtMDhs/9Oi/diQbNCs29Gy2Hdr28s4oQQUCoD8ilmSYZy+gxN9QbL47+hWb9BXb2hVr4oWgSo5WOYJUMIcE5fWgnbuhck6QRJUqLvzrA++xX1+hXq9RvkxR6m8xdYHPyANJsiy2dQ6nt0zTHWZ7+iOvsN9foVrO1QTp9iOv92nD9L0wnKySG65ujSezWmgfcGQtIHTQgebX08tmCSIA8IIUa9SDWKyqcv/hV5sQ+VZKOwL8p9MnVx+oNnQId2zMSXsbK1xvrsVyyP/wrdVzC6ih0Z1KIqZYL9p/8EPPsTze2VJCiney/j7NryymNJyATF5JCOo8W3mEyfQfdrdO0pTo//iuW7/4AxNQnxIfNHJlAqxXTxLQ6f/gnTxUuqlJYHmO19R8Yncbs/RKJyqCKB9w7eG3TNMU7e/gX1+jV0X9GcHegDX8oEWbHA4fM/QUg5Gr0Uk0PsHfwRznRo1m/fs6p28XWgdQVZvRvPUmf5DDPzBwghkRf7SLMJibjmFOvlL1ie/P3K7Q5RRAfvHle+EcMwXyUitqiL0XDrkRPnzse8tC8EoSQJrklOLY+zEmpeIJmVFAI+L+LPSiSzAmpejsJuzJKTIj4/4vKfKVqXfGmwiGMeFCEVFBT69hT1+jWq1Su01RH6bhkHiYfgSwmrWgABxeR3JGk5Gp8U5QFmi5cwfYV6/eqqe0KazTCbfxvnvqZwTqNevcJq+RPq9Wt0zUlcYG5avYRUUDpFlb+CUjnm+99ThSqfIyy+Rd8toZa/RFF1s3ZOGkoOVJXybhRaXXNCTpEXFrjD3Nc5t8prKhkivtEDVP2o16/Q1kfR9ZEeW17ukwuWUEjSAgC119HzcArnaS5QqQzWtiQIkhxJNoVU2aX3G7yDCx59u8RaSATvobvVWFF9b+EuRKzOGbT1Ebr2hFprs9lY+TO6gRZr4IPzaQJCJHCeDEvq6jXq9Ws01VtY212oXNLz01RvkGZTCCmR5jOqxhX7aItF3EeX3hOkSlFMDjHf+w5FeQAhFfpuibOT/0S1/BVtc0TVsK19tL0P05TuUyVZrIo9hXM9mvVbbHklX/1YpYSAhDEtuuYY9fo1zRDWR7C2e+9+vTeoV6+QJAWEUKPpSjl9iiT75Yq7DPRagIM/d98K1nbwnmYGaeSAxOTQQsowDLMLUKVtGE/YBVEktr4eMdGIRKYJRKreq55tV9xElpxzlFSTLQfKi/lxk811kcTZty9IzH4MLOKYR0HfnWF18iPq1W/U+nbBwj8ED+c0dF+hWr2iykZsK8uLPQTvyLHyUugMzNDCOJk+g1QpuuYEq9OfsDz5O4yuyEzkwmqWhElAU72FdwZJOsF87zsk2RQTlaJevYZKsiiublZdoH52alPrmmM067eoV7/DWX3pbYTgEVwAhNukhN1wpkjrGs36LYypyVbfdOhxCtNXMSaBcE6jbY5jqyZVGG2MIfBOj3OJSVJcI3ACQghUDTL9aG/vg79chEWbf40KXXuCunqL6UyMDpppPodql9QeeaNHS4+jrt7g7ORHNPVbaF1f8pwGhODQt2dYL39Gmk4wnX+LJLpAZrGl8zJEfB7KyRM6DtICwXs01Tscv/0LdL+CNd17+2fYh7pfYbX8GUJIlJMnKIo9FOUBvDPRzVKNFasPYfo11sufsV7+iq45vXTWLAQPZ3u09bsYB7APLF6SA2sRSNjd4vllGIb5Yogz6l9iheZBiW2ocpJTRW1eUoVt6yvZupRlPoo7xE4eoSRdV5L2T7w+Xj52IXtPsIhjHpTBkc/oBm1zPGZYXVqNCIHs0ttTJGmB+d53EHFuLS/3kaQlhEzey3CTKhkdAbOcMry8tzC6Rhezxah6cflSNgRPUQbeUVteiM58okSS0cycUtmWXfzVDIHIAg5a16irt+i70zhndt1SOpDL0vVP57ltHrLQNrOF0bzFtJttHTLobD+aV4TgASHIcdDpKKjJnleq601VAFB0wA1nnqiqaGBNC91XKMoDAIiGLpSHc6O2kdi26J2NVd030N31s1fWtmPbZAguulxGp7LLPtSFjAYlU5rly2fw3o2mHl1zEp/Xq8Q87Y++XaIvl+RsGttr0zh7qZKCbsNdcxzFxzrMj1Llr73yfr130H0FlVB+HkCviQTllVbWDMMwXwXXtOp7Z2B1SyclH0EbeF+doK9PYPvBNOseEVFQxdk1kSrIVFE1LVUQSRK/V5BZCpElUNMcapLT5bSAjJf08833Mk/pttXnF9PBBwTnEZxD0BbBWHjt4iV9r98sr/8MfkSwiGMelKHq4FxPBhQfcK4Lw4K0OYUzw4I0Q5KFGCqdwgPn2g2lzMYWssGp0ZkWRlfRLdF+sF3POwMT89oGgURD0RnSdAqVFtGp8mYv/BACtf2tfv+kwPKrbnucPfL2XItoCB7Oh7HS6WOF0zmDELa2PwA+ePq9Ya5LxGiIO29fCDEIux/3mxjOkN5ygDt4O5qr2OvMX4ALgnbYnzQvJy4Jk5FCIo3RDcMJA2+7OPNHuWfhA1lC3jsE28GYFs7ZeL8ixm1QSHrow2jocx3OdOiaU/TdKlaRr3hO4Mc5ymF2kwR5zM3hM5oMw3yNxM9KBA+I963nnenRrd6ir0+35rofDtNVMO0Kul3euzmUUAKySKHKfGtGjebT1Iy+H2fYongTiQISCaFi62MiN0JwEG2JojiA+2qLDAFBG7hGw60a2FUDu27j9RZ21aD922sEsxvmWyzimIclVmKc1XBOf/CNaWgPo8W33rjwyZRysFRO1b2t2xkc+ZK0jLlrKlrST6Jxx01yQ+gNhhwtN33YMmaUKZXB3OJNKMBTJahbwkYxepcMc3M+eIRzU01h/OAKIcYOeBsFaHjv9xD8aLIS3Ujw4UW/GK2QVZLHfZPQTF+c5xqHjeOM4HT+AnmxQJIUm9sA5ZTd5FkdZrOcM3Cmg9HNBwV18B4eekuoDrlz8vKHGN0e03wGpfI4YK4gpCKjkMMfbiDi6YYns29GB1G6X5q1U0kOea1pzSDSHZzT5yMFrvwDErfO6dEoiDLg8BkEOcMwzG4Q4olK7z3kJS163hn07Rmas9cw3QpWX//e/LlxtoczPWxf300lTggSVjE4e/O1qbjJ4WdZQnNqkzyKtvg13b4s6bLMICfZ5tm8h8+Z4H2MGnAIxm0ujYUfr1OmnG97uLqHW7dw65ZE3NalWdZciWOYm+CDg7dRvN0wg2UUKN7EPDPKVRmCO723cFtuDTKGiqukGN2o0myG+d53yIu9D1ZstiliGPfwZj+6W43ByDckAN5RvtbN4wluQVzof6jCSM+lj7978fk//70YJdX1j1NKBZnkyPM5ivIQWbGHLI8iWmWQMqXfkVQFGvYP5fGVW7d0C1EcPFXznIYbK6IfOp4GMbv9u1c/PhFD19N0Mh5zSQwyf/L8T5gv/nDjemqSFsjLg02lMVY5P5QBCMTXTKye3qz6G0bBfm4ff2EOZwzDMLdh+NwI3gGXBGh7b2H7Gn1ziq46huk+7B78OaHuGjrx+slZm1JASBHbHQvILVGWTC+KtHwzt5Yllwu+JLZXxirbfROch+8tfNOPYsytWth1A1dFgbZq4eoevtPwvRnFnjeD8LOj0IPfjUlxFnHMgzIIshAcbjHxFSt4ZMs+BF9SiHb2XmVNjNWydLTohUyQZNQGeVPxOEAtFSZe12OA9u2glkYSsHffZx8Q7dyvek5DIOMRYFzcX/q7F390jYYbohCynPL8iskhislTqrDFLDYhVGyTHBw0xZjVQ0HmHzujFeCDja6J52cir/yLOI+5cUAV40O87EFSi2dKRjbxGBNSQQqBvNhHli9uvdXDjJozFIJ+o+NoPIlxU7EKjEIOYXycnzG3lmEY5tEzRNh4byGRvv/+G8il11kN29cw7eohNvPuEQLZ0wXSZwuqns2K9239Z/H6nK7LIrt3gUYnHwOCD4B1CDbOsg3X7ea66w18q+GqDm4dWyPPms31VRNFXIdgLAWAfwGwiGMelrBdDbnFn5372xC7/C4PwhwEwyAaQnCjaQf1uX9873PfreIs121EaHwMo4D9DG8mYfMG+OFfvJu7TNIJivIQs72XWOx/j7zcH6ufw3M+tsGem9UTyPIZ0nwWQ9znt7/zAGr9vFhtumOEjFEBcWbOOwvnDZxpP6kt1ugK1tRw/pIohguE7TkOhmEY5qMI3sEZTSdmQ/He/wshIZPs3Im7LwGhJKb/8j32/9c/bypr0aBkMCfZNioRaTI6Q94rAQjWwxtLlbWqg6vi5Zqu26qj/2s1fLtVYdsyKqFL+lkwjkThFwKLOGZn2YimIRj7ula4GPoI6p22tofuV9Ga/eOzrdrqHYwegp1vKUQH45DPwqbC9MFfw03E3nXQTFdWLDDbe4m9gx+wOPwBSVLAOQNrGsp76yuYvopzXJqqZt4BQqCcPkUZXAxC/wgRt90y+NkM8wdnUREPswDnDayu0bWn6JrTj75vayhg3Jn+BsfEhVlFhmEY5tZ4Z+BMQx0xl3wGCqmg0gJJVt5wdn5HkAL5d4eY/89/2sxF32NrPX1WA/A+OkVuvrD9vXXwvYHvNOxZQ5W1FV3aVQN7VseftXBND9+bnWmDvCtYxDEPy9BWd0sXQvpTGWfRZNQh/r2wboDE3lDto/cNWng31VucnfyIrjn56M23piURZ/vRNOJrg9ooU0ymT7H/5L+QaUdSwOga1eoVmuoNhY33q80c1zCHhzBmsiVJDlfsPeyDuRY6jvzQfhoCnO0orP30Z5yd/njpQuAmDJEXdoyDYBiGYT4n3mrodoWs3EdWvv/ZI6RCkk+R5nNIlT3AFn4mxPDPA81ER5t/32m4pqfKWt3Fy/Pf+7aHazWCjhU1bWj2bbiuLUUF2Mvm+r98WMQxDwq5AUbnwlu8oVCLpIp/G2d8/OXtiRsjFBINY3B4t0K1+u2akHDmJkiVIs0mKCaHmO39Ic6GBeh+jfXyF6yWP6Gp3sLo6sq/z4oFnH16Lhri0RHnI2jujo4xikbo0NTvcHby4+dpjWW+CsINWpvZhmYH+QoXlruCsz10u4LV9aXv3VIlSPMZ0nIOmWTAlmHaFwFZFN/JTY0nMIcKmw/jZfB+FG7wIRqJGLiqp4rasqaq2nJTXaOfNfBtD9/xic2rYBHHPChCKKgkg1IbMXaTv6HqTwIl083MlbewQ+7b1httcJZyuWLGm1IZuSGmBaTgl8CnkiQFivIQaTaDlIrCvk2Hrj2lKlxzDHeNAyiZ0qRQSTlW5R4jARQ2P+StCQAqyZBmM2oDZZhPIYz/XA4ruB1kaHq+Yr+KwVCJd+5D4EwP3Sxh+vpSgzEpU2TFHPlkH0lGrsQUScMn6y4lCjbf9JTDVnd0vY4VtrqHazpqfWxInPk+VtZ6g9AP39PPgjbw5mED1h87j3fFxHwVDE6FUmZjRe56YwcKRpaK8sdo2Di+cXhDbY0XjEq8t7C6gbMdgncxI66IlvcptXLym/JHo5IcWbFAmk0ghKLWQNOgb8/QNsfQ3dn1NyAElMqQpuUnuFN+fkKgWUqjGzhnAMTtzqaj8+bG8fJr4mbRE8w1BAAY2nQv/xUx5BhyLMSOsB3tcdn/xxlbITjp44FwtoduzmC7iqIGotHW6FSsFJJiiqzcQ1rMoLIS0B280w+74ffIuQrbtltkHCmA3/xsMA8Z5tfssoZdVjDLmq6f1jDLajQpCSzQPhkWccwDQwsTleTIiz04OwQ1X/7iljJBls9RlAcUD4AQXSYbWNPFAPDzf+uchu5X0LoiAxIASuVIsxmyfI4sm1IFj2eRPo7Y2gpsu3/qSwLEL/tTytlLsgmych9J+r5D2GMheA+rG+jujKpxgeItknRCzprFYjwOv1ghNzjCejKQEdhy7OSV6EdDVucO3jsovH9CSQgBoVLIL8wl70smxPbry+a0B6RK6ATQI+5A+JJxpkffLKmlsq+Q5JMYRTS8xuL6JCtQLp5Dtyu0Z6/R1x8/R7+TBBJogwPkUEnbvnRNT+6Q0WDEdQahM2Mmm+s0Vd46g6DNzoRpP3b4nYN5UIZWkiQtkBd70SREw7mrRVyez1FODpEklPHmrIbp6yuFmHMa3juYvoI1HULwkLGCkhd7yPJFXETdVMRdXKx+oQv2GyLGs8lD/hi5boYLba2X/qVQUVDTvlDJ421LDMHBmhZ9t47RFAZSpUjScjyx0IGOxw/FBGzYPpZ24TiKzphjOHwMKVcpi4tPIoyxG5eX4gRlKaY5P887wzBDa6+ci5MygUoLaiP/wsatdgFve3jbQ7dnMH2F1CyoOwgxBzSuT1RSoFx8A2c6WE3h31/KrOPoFInzl2G4PrZIkkOkidVfBG6dAAAWh0lEQVQ1e1rBnFRUXTul7+2qgVu3fBzfIyzimEdBni+wOPgBAOBcj9C50YiEoEVMms8wXXyL+f73o/jquzPUq9+h+/XlNx4CAjx0X6Fa/Q4pFcrpM6TpBPOD7wEAq+XPCCGMFaSrUEmBJJ45lVLBmg5G17dYtH95eG8oNsBpBHgyKom5b8NZ5uAvBHALiSTJMZk+x3T+ApPpc6gke9QzccAmHLatj7Ba/oLJ9CmKySEm02d48vyf6TjyfszDu+rTTMgESVJAqVhZCYGy4uzHZ83dB0NLKc0FWggpkWQTFMU+tZWqPFZgv97Xw8fgvaN4Cdsj+Ol7/y+kQlrMUEwPYdo1rp4wZR4LwXt429N+zd5/PQghkOQTFPOnMH0NrFnFPRSmq1Cf/kaz9gcvacxiC5lkKGZPELyD6evR1dL29efdsMG9m76523zQ2Pk/zKJRla3f5K21Gq7tt67Hn3fx+06f/79OI/SWD+F75nGvmJivhqzYw96hgncaTf0OVrdjkDdAbXdKpcjyOWYLCpNO0gIheOjuDNV1Ig4AgofRFerV70iSHFk+R5rPsdj/I5TKSIz1FXTwwLUiLo8VowIqydC3SzjbXVk5/BoYsuCc7YEQyOQjmyHLZ0gSOsvst/blpoW2wHTxLQ6e/FdMZs+hVP6gj+NmhFHErU9/hlIpiskTOimQzRCCR9cuYzXSXNlOKmWCNJsiy2eQKqMW1OAev4jzns5GDyJOSKTpFKH0yLIZVFIAX/nr4WMI3sHZfjRfuoiUCmkxRz57gmb15gG2kLktIXjqKjHdpaYZEBJJPkUxf4p2/Q4Cgte/D4TpScQlaYli/hS48FEkVYp89gQyzamjRzd0su4zi7ghfmk0v/G4GwfkMEQvBfjOwK0aqqadrGHGClu8jPNsvjPURj9W7pjHAIs45kGh1kk9DgqXs2d49u3/gO5XsLqB99TiKGVKLXflPqbzF5AqhbM9jKnRVG8+LOIAGN2gXr+GlCmStCDhkBQoJ09w+Py/oZw+ge4rWEMzeSH4GGMQ3TBlQvNP2QTemeiE6SCaY+ArXrM626Fvl+jbJXS/Hp+zcvIUT775F0xm38CYGt4ZaruMVagsn2EyfY683IM1DdbLn5EVe8gfdVYcVU269vScQU6S0OV8/x8gVQrdr2D6Gi4Gmm8iMehYUkmONJ1ASAVne/TdErpbPfoKy2ha063QdysYXcfXRYHF4T9CqARWU7j7cKpXCAUhJIxp6ESJXsP01eOOk7hnvDMw3Qqmq5BPn7z3/0IqZOUe/J5BVx1D10tqPdftA2wtcxOCszBdBdOtkU8P3vt/IQTSYo5y7wW66gTd+gimW8Pq5otp1dsVnG7Rrd4hLeYo5s8ghCI3yoQqcsOogFIZJvvfjq/HfLoP3ayg2zN4Zz56rl5IiktK0mIMF1dZibSYIcln6Nbv0K3e0diIrj/5+AjOo/nzrzj63//nXMVtmGsb59uGypy2NMPGx+Wjg0Uc86A4p6H1Ggh01qmcPsVs8ZJco/o1nKWWNJVkyPIFkmwKJRMEBPS6RtecoKneol79/sEzVNY0qGOlI8nICXE6f4GiPEBWLLB3aGD6amwN9N5CqQxKZlBpDpXkZIWvUrTNCdr6LXS/iqYeXy/WdHBWo+/iXEE6QaKmVJ3K5zD9Gl23hLP9aGKT5XOk2RRSJgghoFr9hqZ6h8XB949exIVAIs7ohgRcNsF0/i3yYg/z/X/AbPESRjcwuqLqitOxkpxBJfE4UhmUSuGcRlO9hTgTqNePv8ISgoPRNfruDLo7g+6ruC8n2Dv8AbPFS+h+TSdUYhvzEAbf1ceo1q9Qr16RiyyLuJGhPYvE7fsLQSETZJM9SJWgW79DXx0jrAOLuEeM9xamX0O3K3h7mZshibgkm6BbH6FbvaUZb9NxO/I9Y3ULa3ok+RTd4kWME0hHETcgkwyT/Zcopk+QT/aRT/ZRHf9MDth983EiTggIqaDSAlm8zXx2iHz2BOX8GYr5U5z8+v/grUbwFk63CJ961th51H/+Ge3fXyNYR19DhW1woByvg8XbI4ZFHPPA0NBs356ibU6QpiWKyRMkcbGbJEV0v6NMuOAM2m5FrZHrN2iq12iqdxfm5665r+DGEOpBeBSTw41NvFTI8jm1ciKMtt6AAAIJQd0ZNPU71OvX6NtlNPD4miHHwrY+wsm7v6DrlphMn5FIUylUWqIQcjMXF1tDdL+G7tbQ/QptfQzdr1CU+w/9YG5GnI1rqreAAFXRuiWybIY0m4LMekpqLxzdBuVoEe+dhtU1tK5Qr+kYtqZ5wAd0GwKsabE++xUAMJl/g3LylASqSuNsXBbbof34NzT7F3/Gi4JzONujr07QTw7gdKzwn5uHiS3laY7p/ksICJTVEbrqGM70MT5leL7JcEYIEVux4pdU0UWUjkFndaz+rO73wYqtqISt7Rvef6+KqhhOhCRZGWcH3TgjFHzYeuyPA+8MdL1EXxzB6O/gHc2QXtynQgmUi28QXjoU82foqiNY3cKbPs6XXtinEPF21JYzLLnDeu/QV0fo69P7fbBxfw7tf1Kq+N5fXGvEI4SASjIksbvFWT064GJswb+PfRqA4KCbM6zf/Ri7cAQgBGSSjrPaQ0cF0hz59IDcibMJisVz2L6h7iGn4a0Zu3lGs5DxuN+8HqVKIVRKWblJjiSbIMknSPIp0nw6inyVZJt1yB3htSXx5mNMALOTsIhjHpQhCLWpj3Dy9s9IsykW+3/EZPYceXlAb2Ayo3ZwZ2B0HQXUG6yXP6Na/X6tgcRlGF1htezQxWraZPock/kLlNOnyPIpknQS8+skvCeDFe807NA22J2hqd6MIs599SIOAALa5gjG1Jg2JzAHm32YphPkxQICks5YOqqyds3JuA9tzPBbHHyPx7QQu44QHNr6Hfp2ia4+RrN+jcn8BabzF8iKBbJsCrllXELHkaFIDN2g75Zom2PU6zdo63fQn3tI/g6xpsXq9Cf03RILTa2yRXmIvNyP2XmTUVQ4t5n1cs7QwnRH9vF94UyHvjpGN9mneRtnAZW8lx8mVYrJwR9QzJ+hr0/Q1SfQzRK6OYO3mjIy4/gMLfITSEWVUJVkkNE8SAgF3a1iy989irhzbcX0JVRCrWRZMeaGXv63JGKTbEKPNTrgBucQhAUZez4eIeetRt+cQmUlbF/De0chLEpe+E2ByeIbFLND9PUp+vqEbO+bMzgzOC5HERcF27BPZZLRflUphJBw1mD5+5/vWcQJSEEnWoWiY06pFDLJo/Pmxce7jYRMcyT5FM5qCEk5o0M0A0ahfj/71HQrrN79DT5YZOUCSTYh8XTBcEsIibTcQ5LPSIB7C6s7ONPC9DVV1K2JzqQk5AbBPXQmqDSHykqotKDujCjUtsXwEN2yLfzvDB/z3pidhkUc86AIABAC3hvovopOjwFdrGpQJY4OU+8NLXa6M1oA18dxfu12b0QheASnoXUFREt4rSu09duxejKcMaUQcUcmFc7C6JrayfoV+pgX9qE2zhACjG1xdvJjzNcCvNeo16+H37jdk3YFzhmsl79AqQzOU39+Xb259AOwa09w/PrPSNISzvZo67fUa39hu61tsTr5T1qAG8rwa+ujK+8/eIdWvgMA9O0SaTZFkhaQMt2IYmdgTA3dr9HWR+iak7G1bnX6E5TK0TbHaOtj9N0Z/CXPb/BkAtLU73Dy9t+oRc9pElTd8hbPWkBbH+Ho9f9Fms0AAPX6FcwH5isHvLfw3qJrl2TwYnv03RnSbEJh8kOA/Wg3HoWcJVdTrauxDfWqAFkXn6/V8pf4WqBjtlr9BndDI5QQKE9xdfozpBxiHAKqs98+alA+BAdrW6ANqOSvcLajrLxsRgtxmYxn1H08FqkF+Yhe45cZPXzFBO/hgoZpV2iWv0OlOYrZU8iCjsmNsBFx0U7HgUwyyiec1uPiN/5aXBCqsQInFc31CikRAgUZy/RuzISkypAUU6rCZiVV1cbF6Nal3KpEXKgklXvfIM1nkCq5dNGq0hzl3gsISXOX1rRAPFFAi36/qf4OeYYxbDtEz3TTrdFXJxQ1c6PujY8nhABvDUxXoTl7jbRcoJhRGx6Ac2JVJCkkUoQQyIW5mMNOD+Gs2XR6CACQEFJCChK/UibxUtH7tW4okPoOkCpFNtlHWsypGhWFIm33RbGxVe0Vcty2cvEMKptcfR9Jiun+S8B7ei+NgdtDlZWMNK7Yp/HL9g1VL+/gJBjdf4NufYSzt3+FMz3KvW+QTw+i2KL3zrEiF6uMIQTIJId3VEXLzCKeZBgiQ8JYdZbj65FEuIyv56ucmbcNwRjmIizimAdGQEDSG7S30FEkybNkNEQY37vGRaEbF8+f4tTknUHfr0dhsmk1Gu4zWj4HIMCfu/8QXLy8ieVvgOkrHL/5M5bHfxt/ZnWLu1xEONdjefxXanOL80hDLt5FmvUb6G5NQhU+GrVctNUIMLrB8dt/w/Lk7+OH5pXCIXj4ENC3ZLogZAJ50V0rhFHQUCi4JYEcb+L06K9Yn/0WP9AN/d8llU7vLYxu4Nxv6JrjKBpo39zc4THEebzf0bXLceHoo9vmbRjcOXW/glj9Rmem48L1/HEUthYjtFjxIZ55vuJkhHM9vDdYHv0V67NfaMtDiG6GN3+sznY4Pfp3rJY/b213+9HzN8FTbl69fnXh9TM8ZsTHHV3QPO1rEhsc9Hqe2OrdrbA+/glCSqi0QBpF3PsIqGwCmeTIJvvnF4vx/+lCxIvYhicEhkxMZ7r3rNQ/FpXmKBfPMdl7gWL+nOb3hkqbTDatnFJBnNs2ETdxKwdPqHG7t0nSAtOD76jyca7dDuOJqmGxP34+DGHb8QRKffIrlq/+Ar8+gg/hM8+e0e1bXaM+/TU+H8ko4i4jSSl2JCsXW+2xdFtX7VMIel/1ztBxc0dZmzLJMdl/idmTf0BazJHm07gvk619qwApaZ9unWgYtk0mSWwpvxylMsyefI9y78X4PjEew1v7NITz+zE4N35GNGev4X/Vd+QUSZ9NfX1Kc6rNGfadhhCChNw1z62MYlolGUIx27zfA+Pui3suntiIl5CXHu8McxNYxDEPziYkevPhez+QcHSwcO7z+gKSIUQFo6vPeSdjpfBDOKdjG+qHbvK22/1p+9Ca5oYCihZIzpI1+6cwWLt/CoMou8yU4pOJi03jaxjz8QuVEPyNj4/b3KazPdyj99XcDZzp0K3fjbOkwXtkJc3FYKut6mIl4Db4WK27tnXxlkiVIM2nyCcHKBfPkE8Px5b0cWZLUsVmw+3um+aPSgCXV5pGARCwOck2VnTcWLWhOS15b+Ha3mr01TEt8LOS5q6LOQn0i3OPgzDC7cR18J7mt+TlVcyPQaoEWUlujVm5h6yYn5/BG+b7xhM3t2fImUyuqdZtTnhtTnxh6GrwFt5pqORu42m81dDeAUKQAZXtUc6fI58dIsmnSLJyFLPAtqDGGBT+sQzHMXW/9HCmg+7WcDHW5bG0CzOPAxZxzIMynpXlVgGGYb5ynNXo61OaVQmA0x3mz36ATHJaMD7St8lt05E0I1OGseJAv4D72XgxzgMqIRFUOlYoQxhciQehcz9PpveWZhadAyDhTI/50x+gsmKsXj9GhizPNJ8hzadI8gk2+3QoK93PPhVC0SxhCOM+lbFCl2QTCHXXS1mqyJl2hZX9EV11jHLvBSZ7LzA9eIly8RwqLT7qJMqN7j14eEfHTV+foK+OYboqzhaziGM2sIhjHgHisa5NGIZh7o/g4a2Gac/QSEnGHcHB9HWsWJQ0S6NSmmmTyYVqDpWXQti07WKsEjsERy1oVrfR2fKOwuWjTboYZrTuqE3zdpuw/SkyVEXOs5m3u8dPnNjqaPo12tUbag8PHt72ZNwV88iUSjfVnUvb+s/vV2ox9GNbodMddHNKOXN3xDhLGY+5h2B7HnR7n9JzICHirN7dE8bsNxfdUJ0h8xLdrmKG23R8bugrie3A2zODcdu39+EwUhBn7kNs/aX703CmhzUdGdzUp2hXb6n13XEljjkPiziGYRiGeUSQPf0pnGnR16fIyh9RzJ8inz1BVi5G6/GxrUslceFIrXUhtpqFYYFvNZzuyBCkb2C6NbrqGKa7mYEP8+kE76BbcpzU7Qrro59QzJ+imD1FVs6RRjfEMaNMJRhmxjfmLZtZMG81nO1Ha3vTraGb5f3HC3wFeGfQN6cwuka3foskmyCb7COb7CEtFvE1OSNjnugWOu7DMMyiRvEdDVyGuVRnOsqpi/vQdJQtOFTeqKWyjfmBPEvMnIdFHMMwDMM8IsZZQ6thupoWkH1FFYBRxFHQ/OBQOMzlkPuuQwh2jEfxo4hro5Crobs1TH83VRtvDXS7Qrt6AyBAN2d3crt3TbN8DavraJZ0zxWNECgawcaIkfoEpl/DdKtRCGxXW8XQ9hnNaDYmLSQCxgX+IAD6Grav7kyYe2fR16eoT39D3yyRpHfjenl3xHzS5as7rT5efld+FFymXUHIBGlzRuK7WCAtacYxiyJOqI3j5GBgEkIYT7CEOMtHAm4QchsRZ7o1TF+PRj0McxUs4hiGYRjmURJiG1c/Vs5kXCCeb73bcgfcbtkaz/5Hgw8XXRvdpkJ3F1jdoDr5BV11TNlgD9R69yFsX6NvlnBWP6hDavAePhj09RK2b6PdfLpx83xvnw7Othcs9ke3Rjta9PtPNGkacKbF+t3f0a7fUETMZ5r/+jQoVqGvTu73Xj25jnqnodsV5Cod2043Zi9X7cPz7sTbFdbgzFilo+OTBRxzPSziGIZhGObRQi66trd3ZKN+93hnYuj4bTIav2aiu65u4D53Fekj8c6gq46A6vJc0K+bTVWVYR6Sx2mLxDAMwzAMwzAMw1wKiziGYRiGYRiGYZgdgkUcwzAMwzAMwzDMDsEzccy9QoP2Dta20N0K3mkY3cDaHjzEyzAMwzAMwzAfhkUcc48EONuhDx4n7/4dXXMKHyjgsm2Oo5BjGIZhGIZhGOY6RHgEGRRCiIffCIZhGIZhGIZhmAcihCBu+rs8E8cwDMMwDMMwDLNDsIhjGIZhGIZhGIbZIVjEMQzDMAzDMAzD7BAs4hiGYRiGYRiGYXYIFnEMwzAMwzAMwzA7BIs4hmEYhmEYhmGYHYJFHMMwDMMwDMMwzA7BIo5hGIZhGIZhGGaHYBHHMAzDMAzDMAyzQ7CIYxiGYRiGYRiG2SFYxDEMwzAMwzAMw+wQLOIYhmEYhmEYhmF2CBZxDMMwDMMwDMMwO4QIITz0NjAMwzAMwzAMwzA3hCtxDMMwDMMwDMMwOwSLOIZhGIZhGIZhmB2CRRzDMAzDMAzDMMwOwSKOYRiGYRiGYRhmh2ARxzAMwzAMwzAMs0OwiGMYhmEYhmEYhtkhWMQxDMMwDMMwDMPsECziGIZhGIZhGIZhdggWcQzDMAzDMAzDMDsEiziGYRiGYRiGYZgdgkUcwzAMwzAMwzDMDsEijmEYhmEYhmEYZodgEccwDMMwDMMwDLNDsIhjGIZhGIZhGIbZIVjEMQzDMAzDMAzD7BAs4hiGYRiGYRiGYXYIFnEMwzAMwzAMwzA7BIs4hmEYhmEYhmGYHYJFHMMwDMMwDMMwzA7BIo5hGIZhGIZhGGaHYBHHMAzDMAzDMAyzQ7CIYxiGYRiGYRiG2SFYxDEMwzAMwzAMw+wQ/x/j2fpkJHvXqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "text = ' '.join(pos_A)\n",
    "wordcloud = WordCloud().generate(text)\n",
    "\n",
    "plt.figure(figsize = (15, 9))\n",
    "# Display the generated image:\n",
    "plt.imshow(wordcloud, interpolation= \"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickling the list documents to save future recalculations \n",
    "\n",
    "save_documents = open(\"pickled_algos/documents.pickle\",\"wb\")\n",
    "pickle.dump(documents, save_documents)\n",
    "save_documents.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'good': 13703, 'bad': 8461, 'great': 8297, 'many': 6504, 'much': 6283, 'little': 5648, 'best': 4931, 'first': 4668, 'real': 4360, 'old': 3832, ...})"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a frequency distribution of each adjectives. \n",
    "BOW = nltk.FreqDist(all_words)\n",
    "BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('respect', 'reused')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# listing the 5000 most frequent words\n",
    "word_features = list(BOW.keys())[:5000]\n",
    "word_features[0], word_features[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_word_features = open(\"pickled_algos/word_features5k.pickle\",\"wb\")\n",
    "pickle.dump(word_features, save_word_features)\n",
    "save_word_features.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000\n"
     ]
    }
   ],
   "source": [
    "# function to create a dictionary of features for each review in the list document.\n",
    "# The keys are the words in word_features \n",
    "# The values of each key are either true or false for wether that feature appears in the review or not\n",
    "def find_features(document):\n",
    "    words = word_tokenize(document)\n",
    "    features = {}\n",
    "    for w in word_features:\n",
    "        features[w] = (w in words)\n",
    "\n",
    "    return features\n",
    "\n",
    "# Creating features for each review\n",
    "featuresets = [(find_features(rev), category) for (rev, category) in documents]\n",
    "\n",
    "# Shuffling the documents \n",
    "random.shuffle(featuresets)\n",
    "print(len(featuresets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training_set : 20000 \n",
      "testing_set : 5000\n"
     ]
    }
   ],
   "source": [
    "training_set = featuresets[:20000]\n",
    "testing_set = featuresets[20000:]\n",
    "print( 'training_set :', len(training_set), '\\ntesting_set :', len(testing_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier accuracy percent: 84.36\n",
      "Most Informative Features\n",
      "                  sleeve = True              neg : pos    =     15.8 : 1.0\n",
      "                   lousy = True              neg : pos    =     13.0 : 1.0\n",
      "           unpretentious = True              pos : neg    =     12.9 : 1.0\n",
      "             influential = True              pos : neg    =     12.9 : 1.0\n",
      "                 unfunny = True              neg : pos    =     12.6 : 1.0\n",
      "                quotable = True              pos : neg    =     12.3 : 1.0\n",
      "                 lighten = True              pos : neg    =     12.3 : 1.0\n",
      "                   vapid = True              neg : pos    =     11.9 : 1.0\n",
      "                 radiant = True              pos : neg    =     11.6 : 1.0\n",
      "                 insipid = True              neg : pos    =     11.5 : 1.0\n",
      "               pointless = True              neg : pos    =     11.3 : 1.0\n",
      "                flawless = True              pos : neg    =     10.7 : 1.0\n",
      "               laughable = True              neg : pos    =      9.9 : 1.0\n",
      "                someones = True              neg : pos    =      9.7 : 1.0\n",
      "                   worst = True              neg : pos    =      9.4 : 1.0\n"
     ]
    }
   ],
   "source": [
    "classifier = nltk.NaiveBayesClassifier.train(training_set)\n",
    "\n",
    "print(\"Classifier accuracy percent:\",(nltk.classify.accuracy(classifier, testing_set))*100)\n",
    "\n",
    "classifier.show_most_informative_features(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sleeve', 'lousy', 'unpretentious', 'influential', 'unfunny', 'quotable', 'lighten', 'vapid', 'radiant', 'insipid', 'pointless', 'flawless', 'laughable', 'someones', 'worst', 'atrocious', 'polar', 'horrid', 'haphazard', 'awful', 'extraneous', 'superlative', 'candid', 'indelible', 'blah', 'tenant', 'uninteresting', 'existent', 'lame', 'navy', 'fearless', 'denies', 'unintentional', 'unoriginal', 'fairytale', 'abysmal', 'masterful', 'offscreen', 'consent', 'delightful', 'incompetent', 'undercurrent', 'majesty', 'tumultuous', 'voluptuous', 'ephemeral', 'astonishing', 'tedious', 'idiotic', 'untrue', 'sumptuous', 'unlikeable', 'amateurish', 'unconvincing', 'delicious', 'quintessential', 'pitiful', 'sardonic', 'robust', 'sadness', 'nondescript', 'fiasco', 'chess', 'unforgiving', 'pioneering', 'banquet', 'slum', 'saucer', 'riotous', 'lackluster', 'monotonous', 'email', 'integral', 'pathetic', 'intolerable', 'timeless', 'unavailable', 'relaxed', 'comprehensive', 'semi', 'forgettable', 'wooden', 'dismiss', 'underrated', 'dreadful', 'gentle', 'magnificent', 'horrible', 'awake', 'animosity', 'modesty', 'ivory', 'resonant', 'footing', 'infectious', 'negotiate', 'bedtime', 'softer', 'unforgettable', 'perceptive']\n"
     ]
    }
   ],
   "source": [
    "# Printing the most important features \n",
    "\n",
    "mif = classifier.most_informative_features()\n",
    "\n",
    "mif = [a for a,b in mif]\n",
    "print(mif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting predictions for the testing set by looping over each reviews featureset tuple\n",
    "# The first elemnt of the tuple is the feature set and the second element is the label \n",
    "ground_truth = [r[1] for r in testing_set]\n",
    "\n",
    "preds = [classifier.classify(r[0]) for r in testing_set]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8436"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(ground_truth, preds, labels = ['neg', 'pos'], average = 'micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[2155  374]\n",
      " [ 408 2063]]\n",
      "Normalized confusion matrix\n",
      "[[0.85 0.15]\n",
      " [0.17 0.83]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUYAAAEYCAYAAAAgU193AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3XecVNX9//HXe3epooJSxIoFexSwd2NB7CVqsIFoVPwaE2skxsRuTOLPRMUSC0EsgF2siFhQI0gJFhQUwQKCFAEVBGH5/P44Z2EYdsoud3d2dj9PHvfBzLl37j0zd/Yz59xyPjIznHPOrVRS6Ao451xd44HROefSeGB0zrk0Hhidcy6NB0bnnEvjgdE559LU28AoqZmk5yQtkPT4GqznNEmvJFm3QpG0n6RJdWV7kjpIMklltVWnYiHpC0mHxMdXSrq/BrZxj6Q/J73e+kCFvo5R0qnAJcC2wA/AeOBGM3t7Ddd7BnAhsLeZLVvjitZxkgzoaGaTC12XTCR9AfzGzF6NzzsAU4FGSe8jSf2BaWZ2VZLrrS3pn1UC6zszrm/fJNZX3xW0xSjpEuBfwE1AO2BT4C7g2ARWvxnwaUMIivnwVlnN8c+2HjKzgkzAusCPwElZlmlCCJzfxOlfQJM470BgGnApMAuYAfSK864FfgaWxm2cDVwDPJyy7g6AAWXx+ZnAFEKrdSpwWkr52ymv2xsYDSyI/++dMu8N4HrgnbieV4DWGd5bRf3/kFL/44AjgE+B74ArU5bfHXgXmB+X7Qs0jvNGxPeyML7fX6es/wpgJvBQRVl8zZZxG13i8w2BOcCBeey7B4FL4+ON4rb/Lz7fKq5Xadt7CFgO/BTr+IeUfdAT+Cpu/0957v9V9ksss7j9c+O+/zlu67kM78OA3sBnwDzgTlb2okqAq4Av4/4ZAKyb9t05O9Z7REpZL+DruL7ewG7AB3G/9U3Z9pbAa8Dc+L4fAVqmzP8COCQ+vob43Y37/ceUaRlwTZzXB/ic8N37GDg+lm8HLAbK42vmx/L+wA0p2zwHmBz33xBgw3w+q/o4FTIwdos7tSzLMtcBI4G2QBvgv8D1cd6B8fXXAY0IAWUR0Cr9y5ThecUXuQxYC/ge2CbOaw/skP4HCKwXvxRnxNedEp+vH+e/Eb+YWwPN4vObM7y3ivr/Jdb/HGA28CiwNrBD/DJvEZffBdgzbrcD8AlwUXpQqGT9fyMEmGakBKqUP4RPgObAUOCWPPfdWcRgA5wa3/PglHnPptQhdXtfEP/Y0/bBfbF+OwNLgO3y2P8r9ktlnwFpf/QZ3ocBzwMtCb2V2UC3lPcxGdgCaAE8BTyUVu8BhO9Os5Sye4CmQNe4/56J9d+IEGAPiOvYCjg07ps2hOD6r8o+K9K+uynLdIp17hyfn0T4gSsh/DguBNpn+bxWfEbAQYQA3SXW6Q5gRD6fVX2cCtmVXh+YY9m7uqcB15nZLDObTWgJnpEyf2mcv9TMXiT8Gm5TzfosB3aU1MzMZpjZhEqWORL4zMweMrNlZjYQmAgcnbLMf8zsUzP7CXiM8OXNZCnheOpSYBDQGrjNzH6I258A7ARgZmPNbGTc7hfAv4ED8nhPV5vZklifVZjZfYQWwCjCj8GfcqyvwpvAfpJKgP2BvwP7xHkHxPlVca2Z/WRm7wPvEwIk5N7/SbjZzOab2VfA66zcX6cBt5rZFDP7Efgj0D2t23yNmS1M+2yvN7PFZvYKITANjPWfDrwFdAYws8lmNizum9nAreTenytIakMIuhea2f/iOh83s2/MbLmZDSbs293zXOVpQD8zG2dmS+L73SseB66Q6bOqdwoZGOcCrXMcn9mQ0JWp8GUsW7GOtMC6iPDrXiVmtpDwC9sbmCHpBUnb5lGfijptlPJ8ZhXqM9fMyuPjij+ub1Pm/1TxeklbS3pe0kxJ3xOOy7bOsm6A2Wa2OMcy9wE7AnfEP4iczOxzwo9QJ2A/QkviG0nbUL3AmOkzy7X/k1CVbZcRjoVX+LqS9aXvv0z7s62kQZKmx/35MLn3J/G1jYAngEfNbFBKeQ9J4yXNlzSfsF/zWidp7zf+GMyl+t/tolbIwPguoatxXJZlviGcRKmwaSyrjoWELmOFDVJnmtlQMzuU0HKaSAgYuepTUafp1axTVdxNqFdHM1sHuJJwHC+brJccSGpBOG73AHCNpPWqUJ83gRMJxzmnx+c9gFaEKwuqXJ9KZNv/q+xPSavsz2psK59tL2PVQLcm2/hrfP1OcX+eTu79WeEOwnHEFWfcJW1G+M7+lnBopyXwUco6c9V1lfcraS1Cr642vtt1TsECo5ktIBxfu1PScZKaS2ok6XBJf4+LDQSuktRGUuu4/MPV3OR4YH9Jm0pal9BVAEBSO0nHxC/DEkJrqLySdbwIbC3pVEllkn4NbE9oMdW0tQnHQX+Mrdnz0+Z/SzgeVhW3AWPN7DfAC4TjYwBIukbSG1le+ybhj3BEfP4G4fKot1NawemqWsds+/99YAdJnSQ1JRyHW5NtVbbtiyVtHn9AbiIcR03qKoe1iSdCJG0EXJ7PiySdR2iVn2pmy1NmrUUIfrPjcr0ILcYK3wIbS2qcYdWPAr3i59mE8H5HxcM2DU5BL9cxs1sJ1zBeRdihXxP+2J6Ji9wAjCGc1fsQGBfLqrOtYcDguK6xrBrMSghnt78hnJE7APi/StYxFzgqLjuXcGb1KDObU506VdFlhBMdPxBaBoPT5l8DPBi7USfnWpmkYwknwHrHokuALpJOi883IZxdz+RNwh93RWB8m9CCG5HxFaGVdFWs42W56kiW/W9mnxJOzrxKOJaWft3rA8D2cVvPUHX9CGfSRxCuUlhMCPxJuZZwomMB4UfpqTxfdwoh4H8j6cc4XWlmHwP/j9AT+xb4Bavuv9cIx6xnSlrt+2pmw4E/A08SrnrYEuhenTdWHxT8Am9XN0kaDxwcfwyca1A8MDrnXJp6e6+0c85VlwdG55xL44HROefSFPXN7yprZmq8dqGr4TLovN2mha6Cy2HcuLFzzKxNkussXWczs2Wr3Wi1Gvtp9lAz65bktpNS3IGx8do02SbnlSmuQN4Z1bfQVXA5NGuk9Du51pgt+ymvv8vF4+/M966cWlfUgdE5VxcJVNxH6TwwOueSJaCktNC1WCMeGJ1zyVO+t33XTR4YnXMJ8660c86tzluMzjmXQvJjjM45txrvSjvnXBrvSjvnXCo/+eKcc6sS3mJ0zrlVCUqKO7QUd+2dc3VTibcYnXNuJVH0xxiLu/bOubpJyj3lXIU2kfS6pE8kTZD0+1i+nqRhkj6L/7eK5ZJ0u6TJkj6Q1CVlXT3j8p9J6plr2x4YnXMJixd455pyWwZcambbAXsCF0jaHugDDDezjsDw+BzgcKBjnM4l5GIn5ku/GtgD2B24uiKYZuKB0TmXPJXknnIwsxlmNi4+/gH4BNgIOBZ4MC72IHBcfHwsMMCCkUBLSe2Bw4BhZvadmc0DhhFSB2fkxxidc8nKs6tctVWqA9AZGAW0M7MZEIKnpLZxsY0IuekrTItlmcoz8sDonEtefidfWksak/L8XjO7d7VVSS2AJ4GLzOx7ZQ66lc2wLOUZeWB0ziUs70Ek5pjZrlnXJDUiBMVHzOypWPytpPaxtdgemBXLpwGbpLx8Y+CbWH5gWvkb2bbrxxidc8lL5qy0gAeAT8zs1pRZQ4CKM8s9gWdTynvEs9N7Agtil3so0FVSq3jSpWssy8hbjM65ZCV3HeM+wBnAh5LGx7IrgZuBxySdDXwFnBTnvQgcAUwGFgG9AMzsO0nXA6PjcteZ2XfZNuyB0TmXsGQGkTCzt6n8+CDAwZUsb8AFGdbVD+iX77Y9MDrnkueDSDjnXBofwds551LIx2N0zrnVeVfaOedWleUi7KLggdE5l6jQk/bA6JxzKeQtRuecS+eB0Tnn0nhgdM65NB4YnXMuhSQ/+eKcc+m8xeicc2k8MDrnXBoPjM45l6oeXOBd3Hd6O+fqHMULvHNNOdcj9ZM0S9JHKWWdJI2UNF7SGEm7x/LEckqDB0bnXA1IIjAC/Vk9zenfgWvNrBPwl/gcEswpDR4YnXM1QXlMOZjZCCA9BYEB68TH6xKSXUGCOaXBjzE655KmvE++5JU+Nc1FwFBJtxAadnvH8sRySoMHRudcDSgpyaszmjN9aiXOBy42syclnUzIIngICeaUBu9KO+cSltTJlwx6AhX5pR8nHDeE7DmlKyvPygOjcy55CRxjzOAb4ID4+CDgs/g4sZzS4F1p51zS8j/GmH010kDgQMKxyGmEs8vnALdJKgMWE85AQ4I5pcEDo3OuBuR5jDErMzslw6xdKlk2sZzS4IGxRm3criX3X9+Dduuvw3Iz+j35DncOfIMTDunMn3ofwbabt2O/M25h3MdfAbBp+/UY/9RVfPrlLADe+/ALfnfjIACG3vd7Nmi9Dj8tWQrA0ef3Zfa8HwvzxuqpxYsXc8gv9+fnJUtYVr6M4084kT9ffS0HH7gfP/7wAwCzZs9i19125/Enn1nxujGjR3PAvnvy0KODOeFXJxaq+nVLcd/44oGxJi0rX06fW59i/MRptGjehP8+egXDR01kwuff0P3S++h71eo/iFOmzWHP7jdXur5ef3pwRRB1yWvSpAkvD3uNFi1asHTpUg46YF+6HnY4w994a8Uy3U/+FUcffeyK5+Xl5Vx15RUc2vWwQlS5zir2e6X95EsNmjnne8ZPnAbAj4uWMHHqTDZs05JJU7/ls9gqdHWHJFq0aAHA0qVLWbZ06Sp/4D/88ANvvv4aRx973Iqyu/rewXHH/4o2bdrWen3rqnzOSNf1wFljgVFSB0mfSLpP0gRJr0hqJmlLSS9LGivpLUnbxuW3jPdAjpZ0naR61U/ctP16dNpmY0Z/9EXW5TpstD7vDryCV+7/Pft03nKVef++5nRGDupDn3NyXrjvqqm8vJw9dunEphu25aBDDmX3PfZYMW/IM09z4EEHs8464caL6dOnM+TZpznnvN6Fqm6d5YExu47AnWa2AzAf+BVwL3Chme0CXAbcFZe9DbjNzHYjj+uMislazRoz8JbfcPktT/LDwsUZl5s553u2Pvwv7HXK37ji/z1F/5vOZO21mgLQ68r+7HbyTRxy1j/Zp/OWnHrU7hnX46qvtLSUUWPHM/mLaYwZ/R4TPloxfgGPDR7Iyb9eefjj8ksv4oab/kZpaWkhqlqnqUQ5p7qspgPjVDMbHx+PBToQbuF5XNJ44N9A+zh/L8IFmwCPZlqhpHPjqBpjbNlPNVPrBJWVlTDwlnMY/NIYnn3t/azL/rx0Gd8tWAjA/z75minT5tBxs9BF+2b2AiB0yQe/NIbddtisZivewLVs2ZL9DziQV155GYC5c+cyZvR7HH7EkSuWGTd2DD1O7842W3Xg6aee4KIL/48hzz6TaZUNSrG3GGv65MuSlMflQDtgfhwZo1rivZT3ApQ0b5vz1p5Cu+fq05g0dSa3P/xazmVbt2rBdwsWsny50WGj9dlq0zZMnTaH0tISWq7djLnzF1JWVsIR++/Ia6Mm1ULtG5bZs2fTqFEjWrZsyU8//cRrw1/l0suvAOCpJx7n8COOomnTpiuWn/jZ1BWPzznrTA4/8iiOSTn+2GAldB1jIdX2WenvgamSTjKzxxU+vZ3M7H1gJKGrPRjoXsv1qhF7d9qC047agw8/nc7IQX0AuLrvEJo0KuPWK06idasWPHV7bz6YNJ1jLriTfbtsxZ/PP5Jl5eWUlxsX3jiIed8vonnTxgy58wIalZVSWlrC66Mm0u+pdwr87uqfmTNmcM5ZPSkvL2e5LedXJ57MEUceBcDjjw3isj/0KXANi4OAIo+LKFwXWQMrljoAz5vZjvH5ZUAL4EHCWGntgUbAIDO7TlJH4GHC5/oCcK6ZZR0Fo6R5W2uyzck1Un+35uaN7lvoKrgcmjXS2GoM5JBV0w22tk173J5zuc/+cXji205KjbUYzewLYMeU57ekzK7stOp0YE8zM0ndgTGVLOOcKwLelU7OLkDf2L2eD5xV4Po456pDxd+VrjOB0czeAnYudD2cc2tGQEkdvxwnlzoTGJ1z9YcHRuecS1UPutJ+r7RzLlHhcp2aSZ8ayy+UNCneavz3lPI/xvSpkyQdllLeLZZNlpTXNVfeYnTOJSyxO1v6A32BASvWLP2SkBFwJzNbIqltLN+ecP3zDsCGwKuSto4vuxM4lJDmYLSkIWb2cbYNe2B0ziUuibhoZiPi9dCpzgduNrMlcZmKYaqOJVwTvYRwE8lkVuaDmWxmU0K9NCgumzUwelfaOZcshZMvuaZq2hrYT9IoSW9K2i2We/pU51zdVXGMMQ/VyStdBrQC9gR2Ax6TtAWZ06RW1vjLebufB0bnXOLy7EpXJ6/0NOCpmOPlPUnLgdZkT5Pq6VOdc4VXg8OOPUNIm0o8udIYmENIn9pdUhNJmxPGgn2PkB2wo6TNJTUmnKAZkmsj3mJ0ziVLyVzgnSF9aj+gX7yE52egZ2w9TpD0GOGkyjLgAjMrj+v5LSGXdCnQz8wm5Nq2B0bnXKKSGnYsS/rU0zMsfyNwYyXlLxLyTufNA6NzLmF1f4TuXDwwOucSV+Rx0QOjcy553mJ0zrkUSujkSyF5YHTOJc5bjM45l6bI46IHRudc8rzF6JxzKaQ1GiSiTvDA6JxLXJE3GD0wOueSV1LkkdEDo3MucUUeFzMHRknrZHuhmX2ffHWcc8VOqt8nXyYQBnRMfYcVzw3YtAbr5ZwrYqX19eSLmW2SaZ5zzmVT5A3G/AaqldRd0pXx8caSdqnZajnnipUA5fGvLssZGCX1BX4JnBGLFgH31GSlnHPFrUS5p1wy5ZWO8y6TZJJax+eSdHvMHf2BpC4py/aU9FmceuZV/zyW2dvMzgMWA5jZd4ThxJ1zbnXKnSEwzwvA+wPdVl+9NiHkif4qpfhwQjqDjsC5wN1x2fUII3/vQUinerWkVrk2nE9gXCqphJhZS9L6wPI8Xueca4BEuI4x15SLmY0Avqtk1j+BP7Bqtr9jgQEWjARaSmoPHAYMM7PvzGweMIxKgm26fALjncCTQBtJ1wJvA3/L43XOuQYqXLKTfSKmT02Zzs29Xh0DTDez99Nm1W5eaTMbIGkscEgsOsnMVuvzO+dchTyvY6xS+lRJzYE/AV0rm11JWfrlhqnlWeWbPrUUWErIyuUpV51zGeXTWqzm5TxbApsD70v6gpAjepykDcicVzpbvumM8jkr/SdgILBhXOmjkv6Y19twzjVIpVLOqarM7EMza2tmHcysAyHodTGzmYRc0T3i2ek9gQVmNoOQNrWrpFbxpEvXWJZVPvdKnw7sYmaLACTdCIwF/lrld+acaxCSuCWwsrzSZvZAhsVfBI4AJhMuKewF4SoaSdcDo+Ny18Ura7LKJzB+mbZcGTAlj9c55xqgcFZ6zdeTJa90xfwOKY8NuCDDcv2AflXZdrZBJP5JOEi5CJggaWh83pVwZto551an+p1XuuLM8wTghZTykTVXHedcfVBvR/DO0pd3zrmMkupKF1LOY4yStgRuBLYHmlaUm9nWNVgv51wRK/audD7XJPYH/kP4ITgceAwYVIN1cs4VOeUx1WX5BMbmZjYUwMw+N7OrCKPtOOfcaqQwUG2uqS7L53KdJQrt4s8l9QamA21rtlrOuWJW7F3pfALjxUAL4HeEY43rAmfVZKWcc8WtyONiXoNIjIoPf2DlYLXOOVcpkd+wYnVZtgu8nybLKBRmdkKN1KgKOm23KSP+e3uhq+EyaHXQ1YWugiuE6g8SUWdkazH2rbVaOOfqleoMElGXZLvAe3htVsQ5Vz+IhnHyxTnnqqSOX42TkwdG51ziij0w5j0at6QmNVkR51z9kNQF3pWlT5X0D0kTY4rUpyW1TJn3x5g+dZKkw1LKu8WyyZL65PMe8hnBe3dJHwKfxec7S7ojn5U75xqmhFIb9Gf1jH7DgB3NbCfgU+CPYXvaHugO7BBfc5ekUkmlhIR+hxPGezglLptVPi3G24GjgLkAMTuX3xLonKtUTaZPNbNXzGxZfDqSkG4FQvrUQWa2xMymEkby3j1Ok81sipn9TBjn4dhc284nMJaY2ZdpZeV5vM4510CV5DFRjfSpac4CXoqPazd9KvC1pN0Bi83SCwlNWOecq1SeXeUqpU9ddf36E7AMeKSiqJLFjMobfznTp+YTGM8ndKc3Bb4FXo1lzjm3GqlmR8+R1JNweO/gmOsFsqdJrXL61HzulZ5FOKjpnHN5qam4KKkbcAVwQEXm0mgIIbXzrYRUzx2B9wgtyY6SNieMDNYdODXXdvIZwfs+Kml6mllVjwc45xqAipMva7yeStKnEs5CNwGGxbtrRppZbzObIOkx4GNCF/sCMyuP6/ktIZd0KdDPzCbk2nY+XelXUx43BY5n1YOZzjm3iiTuCMyQPjVjLiozu5EwNGJ6+YuEvNN5y6crPTj1uaSHCNcSOefc6lSPB5HIYnNgs6Qr4pyrHxpKlsB5rDzGWEK44DKv22qccw1TvQ6MMdfLzoSzOQDLU06PO+dcpYp92LGsd77EIPi0mZXHyYOicy6riq50rqkuy+eWwPckdanxmjjn6of6nD5VUlm8WXtf4BxJnwMLCT8IZmYeLJ1zq6nvJ1/eA7oAx9VSXZxz9USRH2LMGhgFYGaf11JdnHP1giipdEyH4pEtMLaRdEmmmWZ2aw3UxzlX5MII3oWuxZrJFhhLgRZUPpyPc85llMS90oWULTDOMLPraq0mzrl6IaRPLXQt1kzOY4zOOVdV9bnFeHCt1cI5V68UeVzMHBjN7LtM85xzLhPVg9F1ivzckXOuLlIeU851VJ5Xej1JwyR9Fv9vFcsl6faYO/qD1Lv1JPWMy38W0yLk5IHROZeopNKnUnle6T7AcDPrCAxn5UhfhxPSGXQEzgXuhhBICSN/70FIpXp1RTDNxgOjcy5xSbQYK8srTcgJ/WB8/CAr78w7FhhgwUigpaT2wGHAMDP7zszmEQbZTg+2q6nOQLXOOZeFKMnvZunWksakPL/XzO7N8Zp2ZjYDwMxmSGoby2s9r7RzzuVN5N0VrXZe6QybTWdZyrPyrrRzLnGSck7V9G3sIhP/nxXLM+WVzpZvOiMPjM65xCVxjDGDIUDFmeWewLMp5T3i2ek9gQWxyz0U6CqpVTzp0jWWZeVdaedcopK6jjFDXumbgccknQ18BZwUF38ROAKYDCwCekG4HlvS9cDouNx1+Vyj7YHROZe4JHK+ZMgrDZXclRfTrlyQYT39gH5V2bYHRudc4or7vhcPjM65GlDkdwR6YHTOJStcrlPckdEDo3MuYXnf8ldneWB0ziWuyOOiB0bnXLK8K+2cc+nkLUbnnFuNH2N0zrkUYTzGQtdizXhgdM4lTn6M0TnnVlXkPWkPjLWtvLyc/ffenfYbbsgTTz/HF1On0qvHqcz77jt27tyZ+/oNoHHjxnz91Vec95teLFgwn/Lycq694SYO63ZEoatf72zcdh3uv/IE2q3fguXLjX7PjeXOJ0bSau1mPHTNSWzWviVfzpjP6Vc/xvwfFwOwX6cO/OPCbjQqK2XugkV0/d1/aNK4jFfv6EXjRmWUlZbw9Bsfc8N/Xi/wuyucYm8x+rBjteyuvrezzTbbrnj+l6v6cMGFv2f8hEm0bNmKAf0fAODvN9/ICSeeyDujxtL/oUe55He/LVSV67Vl5cvpc9dQOp/RlwN638d5x+/Gtpu14bLT9uWNcVP4xam388a4KVx2+n4ArNuiKbddciQn/XEgu/S8k9P+8hgAS35eRreLHmSPs+5mj7PupuseW7H79hsX8q0VjBClyj3VZR4Ya9H0adMY+tKL9Ox1NgBmxptvvM5xJ5wIwKmn9+D5IWF4OUl8//0PACxYsIANNtywMJWu52bO/ZHxn84A4Meffmbil3PYsM3aHLXvtjz88ngAHn55PEfvG37Mfn3IL3h2xCd8PWsBALPnL1yxroU//QxAo7JSyspKCAO+NEDxcp1cU11Wo4FRUgdJEyU9GFMaPiGpuaSDJf1P0ocxRWKTuPzNkj6Oy95Sk3UrhCsuv5jrb7qZkpLwsc+dO5eW67akrCwc0dhoo4355pswuPCVV13N4IGPsM2Wm3LicUdxy623FazeDcWmG7SkU8cNGP3xdNq2WouZc38EQvBs02otADpusj4t127G0NvO5J37zuPUw3Ze8fqSEjHygd589ezlvDZmCqM/mV6Q91EXJDVQraSLJU2Q9JGkgZKaStpc0qiYDnWwpMZx2Sbx+eQ4v0N1618bLcZtCEludgK+By4hpEX8tZn9gnCc8/yY5vB4YIe47A2VrUzSuZLGSBozZ/bsWqh+Ml568XnatGlL5y67rCirrEVRMY7d448N4rQzejLp86944pnnOeesnixfvrzW6tvQrNWsMQOv/zWX3/EyPyxaknG5stISumzdnuOveIRjLnuIP/Y8gK02Xh+A5cuNPc++h61OvJVdt92I7Tdvm3E99VlS6VMlbQT8DtjVzHYESoHuwN+Af8YUqvOAs+NLzgbmmdlWwD/jctVSG4HxazN7Jz5+mDDI5FQz+zSWPQjsTwiai4H7JZ1AGIV3NWZ2r5ntama7tm7TpoarnpyR//0vL77wHDtsvQVn9jiVEW+8Tp/LLmb+gvksW7YMgOnTp9G+fXsABvTvxwm/CoMT77HnXixZvJi5c+YUrP71WVlpCQOv/zWDh33AsyM+AWDWvIVssH4LADZYvwWz54Uu8/TZ3/PKe5NZtHgpcxcs4u33v2Snrdqtsr4FPy5mxPgv6LrHVrX7RuqQBLvSZUAzSWVAc2AGcBDwRJyfnkK1IrXqE8DBquaIubURGPM60GJmywgJsZ8kvNGXa7JSte3aG25i0udfMeHTKfQf8Cj7H/hLHnjwYfY/4ECeeSrs40cfHsCRRx8LwCabbMIbrw8HYOLET1i8ZDHF9ENQTO654lgmfTmb2x97d0XZC+9M4vRunQA4vVsnnn97IgDPvT2RfXbajNLSEpo1acRu223ExC/n0Hrd5qzboikATRuXcdAuWzDpy4b7Q6Y8/hHTp6Zzt51XAAAMT0lEQVRM56auw8ymA7cQUhjMABYAY4H5MV7AqulQV6RKjfMXAOtXp/61cbnOppL2MrN3gVOAV4HzJG1lZpOBM4A3JbUAmpvZi5JGEnI31HvX3XAzvXqcyvXX/IWdOnWix5lnAXDT327ht+efx5133IYk7rm3XyLDxbtV7f2LTTmtWyc+/HwmIx/oDcDV9w3nlkfe4uFrT6bnkV34+tsFK84+T/pyDsNGTWb0f85n+XKj/wvj+HjqLHbcoh33XXk8paWhm/jk6xN46d1Ps226Xsvzq5o1fWpMXnUssDkwH3gcOLySRSsaX9VKlVrptmvyzFk8+PkiMALYG/iMEAj3IvwSlBGS1JwPrEfI+NWU8AZvMbMHV1tpii677Goj/vteDdXerak2h15b6Cq4HBa/dd3YBHM7A7DdLzrbgGffyLnc7lu2zLptSScB3czs7Pi8ByF2nARsYGbLJO0FXGNmh0kaGh+/G7veM4E2Vo0gVxstxuVm1jutbDjQOa1sBqEr7Zwrdsl0br4C9pTUHPiJcH5iDPA6cCIwiNVTqPYE3o3zX6tOUAS/88U5lzApmdF1zGyUpCeAccAy4H/AvcALwCBJN8SyB+JLHgAekjQZ+I5wBrtaajQwmtkXwI41uQ3nXN2T1NFwM7uakE861RQq6V2a2WJW5pleI95idM4lr8jPE3pgdM4lbMXlOEXLA6NzLlE+UK1zzlXGA6Nzzq3Ku9LOOZem2G/S8sDonEtckcdFD4zOuYSJor+v3wOjcy5RwrvSzjm3miKPix4YnXM1oMgjowdG51zikhhEopA8MDrnElfcYdEDo3OuJhR5ZPTA6JxLVEiPWtyRsTaSYTnnGhKFQSRyTXmtSmoZ89FPlPSJpL0krSdpWMwrPSzmhkHB7TGv9AeSulT3LXhgdM4lT3lM+bkNeNnMtgV2Bj4B+gDDY17p4fE5hERZHeN0LnB3davvgdE5l7B8kqfmjoyS1iHknH8AwMx+NrP5rJo/Oj2v9AALRgItJbWvzjvwwOicS5yUeyJHXmlgC2A28B9J/5N0v6S1gHZmNgMg/t82Lr8ir3SUmnO6Svzki3MuUVXoKWfNK02IT12AC2NirNtY2W3OtOl01coS6C1G51ziJOWc8jANmGZmo+LzJwiB8tuKLnL8f1bK8pukvH5j4Jvq1N8Do3MucXl2pbMys5nA15K2iUUHAx+zMn80rJ5Xukc8O70nsKCiy11V3pV2ziUuwasYLwQekdSYkDa1F6FB95iks4GvWJky9UXgCGAysCguWy0eGJ1zycqzRZgPMxsPVHYc8uBKljXggiS264HROZeoMB5jcd/54oHROZe44g6LHhidczWgyBuMHhidc8kr9kEkPDA655JX3HHRA6NzLlmqwug5dZUHRudc4rwr7Zxz6Yo7LnpgdM4lr8jjogdG51zS5FkCnXMuVbjzpdC1WDM+uo5zzqXxFqNzLnHF3mL0wOicS1yxX67jXWnnXKIqLvBOKH1qacz38nx8vrmkUTF16uA4TiOSmsTnk+P8DmvyHjwwOueSl1z61N8TUqZW+Bvwz5g6dR5wdiw/G5hnZlsB/4zLVZsHRudc4hJKn7oxcCRwf3wu4CBC7hdYPXVqRUrVJ4CDtQaDQnpgdM4lLqH0qf8C/gAsj8/XB+ab2bL4PDU96orUqXH+grh8tfjJF+dc4vJsq2VMnyrpKGCWmY2VdGBFcSWLWh7zqswDo3MucQmcld4HOEbSEUBTYB1CC7KlpLLYKkxNj1qROnWapDJgXeC76m5cIX9McZI0G/iy0PVIUGtgTqEr4bKqb/toMzNrk+QKJb1M+JxymWNm3fJY34HAZWZ2lKTHgSfNbJCke4APzOwuSRcAvzCz3pK6AyeY2cnVfg/FHBjrG0ljMnUtXN3g+6j2pQXGLYBBwHrA/4DTzWyJpKbAQ0BnQkuxu5lNqfY2PTDWHf5HV/f5PmoY/Ky0c86l8cBYt9xb6Aq4nHwfNQDelXbOuTTeYnTOuTQeGJ1zLo0HRuecS+OB0Tnn0nhgrEMqRgNZk1FBXM3z/VP/eWCsW7YGMDPzP766R9IOktqZX8pR73lgrCMkdQRGS+oLHhzrGknHAHcDHVLKfP/UU34dYx0Qh1g6mTBSyBnAc2bWO86Tt1AKS9IOwEDCwASTJbUGmpvZV5JKzGx5jlW4IuMtxgKTtBZwGfC4mfUBdgR+Kek28JZjIaV87u2AWUBbSX8BHgY+lNTJg2L95IGxwMxsITCVOK6cmc0DLgZ6SboxlnmLsTAqRoB+HRgD3AZMAboD/wB2KFC9XA3zwFggkraRtImkFsB7wCOSmsfZ8wh/hIdI2q9glWzAJHUDHpU0ALgWuNnMdjOzh4FtgNMJP2iuHvIRvAtA0uGELGZPAKcQus87AG9JGg6cREju05SV+S5cLYnHFPsCvYC1gV2BeyRdShiAdQBwqZn9t3C1dDXJW4y1TNJWwNXA8cBkQuBrbma/BS4HRgCHEYZmPxSYUaCqNmRNgGFm9hbwMtAP+AHYFvgQON7Mnvdjv/WXtxhr3zzgEWAX4CLgWDP7QVJXYKSZfR9bLP8Aeq7JKMSuaiTtA2xB+Ls4SdIQM3uJkEdkGSENwHLgY/Bjv/WZB8ZaIukAYDvCwfuLCZ/9lma2VNKeQB/gHOB7QmKfI81sbqHq29DEfXA38AEwk7APrpW0CSEQ7k3oQrsGwK9jrAWS9iB0xyYBnwDNgB7AjcAy4CzgGjN7tmCVbMAk7Q78FbjSzEbFvCInEzLVNSMkXHvOzJ4pYDVdLfIWYw2Lf3TXAqeY2QeSzgA2AwYTTrh8BPzBzIb5xdwFsy5wIHAwMAr4CphAuFznioprFX3/NBx+8qXmtQQOIZxIgXAHxRTCwfwPzexfZjYM/JhVocTP/wTgLEmnxJzFCwjBsnXFSRbfPw2HtxhrmJm9IukE4K+SvjGzgZIGx9nvF7JubiUze1bScsL1pMcBi4BrzWxWgavmCsCPMdYSSUcA1wO3m9mDha6Pq1wcLOIa4GEzu9Vbiw2TtxhriZm9KKkMuFnSMGCm32db95jZEEmLgX6SvjCzpwpdJ1f7vMVYyyS1MbPZha6Hy07SocDnfh1pw+SB0Tnn0vhZaeecS+OB0Tnn0nhgdM65NB4YnXMujQfGekRSuaTxkj6S9HjKwLfVWdeBkp6Pj4+R1CfLsi0l/V81tnGNpMvyLU9bpr+kE6uwrQ6SPqpqHV3D5IGxfvnJzDqZ2Y7Az0Dv1JkKqrzPzWyImd2cZZGWQJUDo3N1lQfG+ustYKvYUvpE0l3AOGATSV0lvStpXGxZtoAwnL+kiZLeJtw7TCw/syKtq6R2kp6W9H6c9gZuBraMrdV/xOUulzRa0geSrk1Z158kTZL0KiFFQFaSzonreV/Sk2mt4EMkvSXp05hpEUmlkv6Rsu3z1vSDdA2PB8Z6KN5hczhhtGkIAWiAmXUGFgJXAYeYWRdCkqdLJDUF7gOOBvYDNsiw+tuBN81sZ6ALYRSaPoSLoTuZ2eVx0N2OwO5AJ2AXSftL2oWQSKozIfDulsfbeSrmWtmZMGTb2SnzOgAHAEcSUg80jfMXmNlucf3nSNo8j+04t4LfEli/NJM0Pj5+C3gA2BD40sxGxvI9ge2Bd+JtwI2BdwnD9k81s88AJD0MnFvJNg4ijCWJmZUDCyS1Sluma5z+F5+3IATKtYGnzWxR3MaQPN7TjpJuIHTXWwBDU+Y9Fm+r/EzSlPgeugI7pRx/XDdu+9M8tuUc4IGxvvnJzDqlFsTgtzC1iJDP5JS05ToBSd0GJeCvZvbvtG1cVI1t9AeOM7P3JZ1JGAqsQvq6LG77QjNLDaBI6lDF7boGzLvSDc9IYJ+YlAtJzSVtDUwENpe0ZVzulAyvHw6cH19bKmkdwtiSa6csM5QwtmHFscuNJLUlJPo6XlIzSWsTuu25rA3MkNQIOC1t3kmSSmKdtyCMkD4UOD8uj6StJa2Vx3acW8FbjA2Mmc2OLa+BkprE4qvM7FNJ5wIvSJoDvE1I65ru98C9ks4GyoHzzexdSe/Ey2FeiscZtwPejS3WH4HTzWxcHItyPCFdwFt5VPnPhFG1vyQcM00NwJOAN4F2QG8zWyzpfsKxx3FxyLDZwHH5fTrOBT6IhHPOpfGutHPOpfHA6JxzaTwwOudcGg+MzjmXxgOjc86l8cDonHNpPDA651ya/w+CFL40ltI1UwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.utils.multiclass import unique_labels\n",
    "\n",
    "y_test = ground_truth\n",
    "y_pred = preds\n",
    "class_names = ['neg', 'pos']\n",
    "\n",
    "\n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, classes,\n",
    "                          normalize=False,\n",
    "                          title=None,\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if not title:\n",
    "        if normalize:\n",
    "            title = 'Normalized confusion matrix'\n",
    "        else:\n",
    "            title = 'Confusion matrix, without normalization'\n",
    "\n",
    "    # Compute confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    # Only use the labels that appear in the data\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=classes, yticklabels=classes,\n",
    "           title=title,\n",
    "           ylabel='True label',\n",
    "           xlabel='Predicted label')\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    fig.tight_layout()\n",
    "    return ax\n",
    "\n",
    "\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plot_confusion_matrix(y_test, y_pred, classes=class_names,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plot_confusion_matrix(y_test, y_pred, classes=class_names, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.classify.scikitlearn import SklearnClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB,BernoulliNB\n",
    "from sklearn.linear_model import LogisticRegression,SGDClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classifiers for an ensemble model: \n",
    "Naive Bayes (NB)\n",
    "Multinomial NB\n",
    "Bernoulli NB\n",
    "Logistic Regression\n",
    "Stochastic Gradient Descent Classifier - SGD\n",
    "Support Vector Classification - SVC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Naive Bayes Algo accuracy percent: 84.36\n",
      "Most Informative Features\n",
      "                  sleeve = True              neg : pos    =     15.8 : 1.0\n",
      "                   lousy = True              neg : pos    =     13.0 : 1.0\n",
      "           unpretentious = True              pos : neg    =     12.9 : 1.0\n",
      "             influential = True              pos : neg    =     12.9 : 1.0\n",
      "                 unfunny = True              neg : pos    =     12.6 : 1.0\n",
      "                quotable = True              pos : neg    =     12.3 : 1.0\n",
      "                 lighten = True              pos : neg    =     12.3 : 1.0\n",
      "                   vapid = True              neg : pos    =     11.9 : 1.0\n",
      "                 radiant = True              pos : neg    =     11.6 : 1.0\n",
      "                 insipid = True              neg : pos    =     11.5 : 1.0\n",
      "               pointless = True              neg : pos    =     11.3 : 1.0\n",
      "                flawless = True              pos : neg    =     10.7 : 1.0\n",
      "               laughable = True              neg : pos    =      9.9 : 1.0\n",
      "                someones = True              neg : pos    =      9.7 : 1.0\n",
      "                   worst = True              neg : pos    =      9.4 : 1.0\n",
      "MNB_classifier accuracy percent: 84.5\n",
      "BernoulliNB_classifier accuracy percent: 84.48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression_classifier accuracy percent: 83.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGDClassifier_classifier accuracy percent: 80.24\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC_classifier accuracy percent: 78.08\n"
     ]
    }
   ],
   "source": [
    "print(\"Original Naive Bayes Algo accuracy percent:\", (nltk.classify.accuracy(classifier, testing_set))*100)\n",
    "classifier.show_most_informative_features(15)\n",
    "\n",
    "MNB_clf = SklearnClassifier(MultinomialNB())\n",
    "MNB_clf.train(training_set)\n",
    "print(\"MNB_classifier accuracy percent:\", (nltk.classify.accuracy(MNB_clf, testing_set))*100)\n",
    "\n",
    "BNB_clf = SklearnClassifier(BernoulliNB())\n",
    "BNB_clf.train(training_set)\n",
    "print(\"BernoulliNB_classifier accuracy percent:\", (nltk.classify.accuracy(BNB_clf, testing_set))*100)\n",
    "\n",
    "LogReg_clf = SklearnClassifier(LogisticRegression())\n",
    "LogReg_clf.train(training_set)\n",
    "print(\"LogisticRegression_classifier accuracy percent:\", (nltk.classify.accuracy(LogReg_clf, testing_set))*100)\n",
    "\n",
    "SGD_clf = SklearnClassifier(SGDClassifier())\n",
    "SGD_clf.train(training_set)\n",
    "print(\"SGDClassifier_classifier accuracy percent:\", (nltk.classify.accuracy(SGD_clf, testing_set))*100)\n",
    "\n",
    "SVC_clf = SklearnClassifier(SVC())\n",
    "SVC_clf.train(training_set)\n",
    "print(\"SVC_classifier accuracy percent:\", (nltk.classify.accuracy(SVC_clf, testing_set))*100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Storing all models using pickle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_pickle(c, file_name): \n",
    "    save_classifier = open(file_name, 'wb')\n",
    "    pickle.dump(c, save_classifier)\n",
    "    save_classifier.close()\n",
    "\n",
    "classifiers_dict = {'ONB': [classifier, 'pickled_algos/ONB_clf.pickle'],\n",
    "                    'MNB': [MNB_clf, 'pickled_algos/MNB_clf.pickle'],\n",
    "                    'BNB': [BNB_clf, 'pickled_algos/BNB_clf.pickle'],\n",
    "                    'LogReg': [LogReg_clf, 'pickled_algos/LogReg_clf.pickle'],\n",
    "                    'SGD': [SGD_clf, 'pickled_algos/SGD_clf.pickle'], \n",
    "                    'SVC': [SVC_clf, 'pickled_algos/SVC_clf.pickle']}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for clf, listy in classifiers_dict.items(): \n",
    "    create_pickle(listy[0], listy[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_score MNB: 0.845\n",
      "Accuracy_score BNB: 0.8448\n",
      "Accuracy_score LogReg: 0.835\n",
      "Accuracy_score SGD: 0.8024\n",
      "Accuracy_score SVC: 0.7808\n"
     ]
    }
   ],
   "source": [
    "acc_scores = {}\n",
    "for clf, listy in classifiers_dict.items(): \n",
    "    # getting predictions for the testing set by looping over each reviews featureset tuple\n",
    "    # The first elemnt of the tuple is the feature set and the second element is the label \n",
    "    acc_scores[clf] = accuracy_score(ground_truth, predictions[clf])\n",
    "    print(f'Accuracy_score {clf}: {acc_scores[clf]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score MNB: 0.845\n",
      "f1_score BNB: 0.8447999999999999\n",
      "f1_score LogReg: 0.835\n",
      "f1_score SGD: 0.8024\n",
      "f1_score SVC: 0.7808\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, accuracy_score\n",
    "ground_truth = [r[1] for r in testing_set]\n",
    "predictions = {}\n",
    "f1_scores = {}\n",
    "for clf, listy in classifiers_dict.items(): \n",
    "    # getting predictions for the testing set by looping over each reviews featureset tuple\n",
    "    # The first elemnt of the tuple is the feature set and the second element is the label \n",
    "    predictions[clf] = [listy[0].classify(r[0]) for r in testing_set]\n",
    "    f1_scores[clf] = f1_score(ground_truth, predictions[clf], labels = ['neg', 'pos'], average = 'micro')\n",
    "    print(f'f1_score {clf}: {f1_scores[clf]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.classify import ClassifierI\n",
    "\n",
    "# Defininig the ensemble model class \n",
    "\n",
    "class EnsembleClassifier(ClassifierI):\n",
    "    \n",
    "    def __init__(self, *classifiers):\n",
    "        self._classifiers = classifiers\n",
    "    \n",
    "    # returns the classification based on majority of votes\n",
    "    def classify(self, features):\n",
    "        votes = []\n",
    "        for c in self._classifiers:\n",
    "            v = c.classify(features)\n",
    "            votes.append(v)\n",
    "        return mode(votes)\n",
    "    # a simple measurement the degree of confidence in the classification \n",
    "    def confidence(self, features):\n",
    "        votes = []\n",
    "        for c in self._classifiers:\n",
    "            v = c.classify(features)\n",
    "            votes.append(v)\n",
    "\n",
    "        choice_votes = votes.count(mode(votes))\n",
    "        conf = choice_votes / len(votes)\n",
    "        return conf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading all models using pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load all classifiers from the pickled files \n",
    "\n",
    "# function to load models given filepath\n",
    "def load_model(file_path): \n",
    "    classifier_f = open(file_path, \"rb\")\n",
    "    classifier = pickle.load(classifier_f)\n",
    "    classifier_f.close()\n",
    "    return classifier\n",
    "\n",
    "\n",
    "# Original Naive Bayes Classifier\n",
    "ONB_Clf = load_model('pickled_algos/ONB_clf.pickle')\n",
    "\n",
    "# Multinomial Naive Bayes Classifier \n",
    "MNB_Clf = load_model('pickled_algos/MNB_clf.pickle')\n",
    "\n",
    "\n",
    "# Bernoulli  Naive Bayes Classifier \n",
    "BNB_Clf = load_model('pickled_algos/BNB_clf.pickle')\n",
    "\n",
    "# Logistic Regression Classifier \n",
    "LogReg_Clf = load_model('pickled_algos/LogReg_clf.pickle')\n",
    "\n",
    "# Stochastic Gradient Descent Classifier\n",
    "SGD_Clf = load_model('pickled_algos/SGD_clf.pickle')\n",
    "\n",
    "\n",
    "\n",
    "# Initializing the ensemble classifier \n",
    "ensemble_clf = EnsembleClassifier(ONB_Clf, MNB_Clf, BNB_Clf, LogReg_Clf, SGD_Clf)\n",
    "\n",
    "# List of only feature dictionary from the featureset list of tuples \n",
    "feature_list = [f[0] for f in testing_set]\n",
    "\n",
    "# Looping over each to classify each review\n",
    "ensemble_preds = [ensemble_clf.classify(features) for features in feature_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8459999999999999"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(ground_truth, ensemble_preds, average = 'micro')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Live Sentiment Analysis\n",
    "\n",
    "Using the sentiment function we can classify individual reviews. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('pos', 0.6)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to do classification a given review and return the label a\n",
    "# and the amount of confidence in the classifications\n",
    "def sentiment(text):\n",
    "    feats = find_features(text)\n",
    "    return ensemble_clf.classify(feats), ensemble_clf.confidence(feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('pos', 0.6), ('neg', 0.8), ('neg', 0.6), ('neg', 1.0), ('pos', 1.0))"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sentiment analysis of reviews of captain marvel found on rotten tomatoes\n",
    "text_a = '''The problem is with the corporate anticulture that controls these productions-and \n",
    "            the fandom-targeted demagogy that they're made to fulfill-which responsible casting \n",
    "                can't overcome alone.'''\n",
    "text_b = '''Does it work? The short answer is: yes. There's enough to keep both diehard \n",
    "                Marvel fans and newcomers engaged.'''\n",
    "text_c = '''It was lacking, a bit flat, and I'm honestly concerned about how she will enter\n",
    "            the Marvel Cinematic Universe...it's so concerned with being a feminist film that \n",
    "            it forgets how to be a superhero movie.'''\n",
    "text_d = '''The film may be about women breaking their shackles, but the lead actress feels kept \n",
    "            in check for much of the picture. Humor winds up being provided by Samuel Jackson's Nick \n",
    "            Fury, heart by Lashana Lynch's Maria Rambeau, and pathos by...well, it ain't Larson'''\n",
    "text_e = '''\"Everything was beautiful and nothing hurt\"'''\n",
    "\n",
    "sentiment(text_a), sentiment(text_b), sentiment(text_c), sentiment(text_d), sentiment(text_e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e907bbbb64a499b94cf046591cf102d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=19999), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# converting the training set  into a pandas data frame\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import time\n",
    "import pandas as pd \n",
    "df = pd.DataFrame([training_set[0][0]])\n",
    "for f in tqdm(training_set[1:]): \n",
    "    df = df.append([f[0]], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fc3aa3ef64a4f04b1c713c9b9a1ce89",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4999), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# converting the testing set  into a pandas data frame\n",
    "df_test = pd.DataFrame([testing_set[0][0]])\n",
    "for f in tqdm(testing_set[1:]): \n",
    "    df_test = df_test.append([f[0]], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>abandoningbr</th>\n",
       "      <th>abandonment</th>\n",
       "      <th>able</th>\n",
       "      <th>abnormal</th>\n",
       "      <th>aboutbr</th>\n",
       "      <th>abr</th>\n",
       "      <th>abrupt</th>\n",
       "      <th>absent</th>\n",
       "      <th>absolute</th>\n",
       "      <th>abstract</th>\n",
       "      <th>...</th>\n",
       "      <th>youve</th>\n",
       "      <th>zany</th>\n",
       "      <th>zealous</th>\n",
       "      <th>zen</th>\n",
       "      <th>zenith</th>\n",
       "      <th>zest</th>\n",
       "      <th>zombi</th>\n",
       "      <th>zombie</th>\n",
       "      <th>zooey</th>\n",
       "      <th>zooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 5000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       abandoningbr  abandonment   able  abnormal  aboutbr    abr  abrupt  \\\n",
       "19995         False        False  False     False    False  False   False   \n",
       "19996         False        False  False     False    False  False   False   \n",
       "19997         False        False  False     False    False  False   False   \n",
       "19998         False        False  False     False    False  False   False   \n",
       "19999         False        False  False     False    False  False   False   \n",
       "\n",
       "       absent  absolute  abstract  ...    youve   zany  zealous    zen  \\\n",
       "19995   False     False     False  ...    False  False    False  False   \n",
       "19996   False     False     False  ...    False  False    False  False   \n",
       "19997   False     False     False  ...    False  False    False  False   \n",
       "19998   False     False     False  ...    False  False    False  False   \n",
       "19999   False     False     False  ...    False  False    False  False   \n",
       "\n",
       "       zenith   zest  zombi  zombie  zooey  zooms  \n",
       "19995   False  False  False   False  False  False  \n",
       "19996   False  False  False   False  False  False  \n",
       "19997   False  False  False   False  False  False  \n",
       "19998   False  False  False   False  False  False  \n",
       "19999   False  False  False   False  False  False  \n",
       "\n",
       "[5 rows x 5000 columns]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=100, min_samples_split = 100, random_state=0)\n",
    "X = df\n",
    "y = [x[1] for x in training_set]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=500,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X, y )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85385"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = df_test\n",
    "y_test = [x[1] for x in testing_set]\n",
    "clf.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['abandoningbr', 'abandonment', 'able', 'abnormal', 'aboutbr', 'abr',\n",
       "       'abrupt', 'absent', 'absolute', 'abstract',\n",
       "       ...\n",
       "       'youve', 'zany', 'zealous', 'zen', 'zenith', 'zest', 'zombi', 'zombie',\n",
       "       'zooey', 'zooms'],\n",
       "      dtype='object', length=5000)"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "\n",
    "# Model (can also use single decision tree)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(n_estimators=10)\n",
    "\n",
    "# Train\n",
    "model.fit(iris.data, iris.target)\n",
    "# Extract single tree\n",
    "estimator = clf.estimators_[5]\n",
    "\n",
    "from sklearn.tree import export_graphviz\n",
    "# Export as dot file\n",
    "export_graphviz(estimator, out_file='tree.dot', \n",
    "                feature_names = df.columns,\n",
    "                class_names = ['neg', 'pos'],\n",
    "                rounded = True, proportion = False, \n",
    "                precision = 2, filled = True)\n",
    "\n",
    "# Convert to png using system command (requires Graphviz)\n",
    "from subprocess import call\n",
    "call(['dot', '-Tpng', 'tree.dot', '-o', 'tree.png', '-Gdpi=600'])\n",
    "\n",
    "# Display in jupyter notebook\n",
    "from IPython.display import Image\n",
    "Image(filename = 'tree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<20000x25282 sparse matrix of type '<class 'numpy.int64'>'\n",
       " \twith 21938922 stored elements in Compressed Sparse Row format>,\n",
       " array([    0,   303,   526,   699,   902,  1155,  1464,  1669,  1858,\n",
       "         2067,  2312,  2519,  2856,  3111,  3406,  3695,  3962,  4167,\n",
       "         4502,  4761,  5050,  5291,  5550,  5793,  6064,  6285,  6506,\n",
       "         6823,  7118,  7307,  7614,  7875,  8140,  8369,  8586,  8883,\n",
       "         9102,  9419,  9648,  9999, 10244, 10563, 10728, 10959, 11152,\n",
       "        11317, 11540, 11771, 11952, 12253, 12506, 12723, 12936, 13171,\n",
       "        13454, 13663, 13924, 14121, 14434, 14673, 14922, 15131, 15406,\n",
       "        15619, 15834, 16085, 16418, 16697, 16872, 17185, 17428, 17707,\n",
       "        18056, 18271, 18548, 18785, 19016, 19265, 19456, 19757, 20038,\n",
       "        20309, 20542, 20829, 21110, 21381, 21672, 21923, 22176, 22365,\n",
       "        22682, 22907, 23092, 23387, 23660, 23985, 24292, 24517, 24776,\n",
       "        25039, 25282]))"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.decision_path(X)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
